├── LICENSE ├── README.md ├── images ├── databricks_ce_create_mlr.png ├── databricks_ce_download_notebooks.png ├── databricks_ce_import_notebooks.png ├── databricks_ce_loging.png ├── mlflow-workshop.png ├── mlflow-workshop1-youtube.png ├── tmls_2021.png └── tmls_sv.png ├── model_registery ├── README.md ├── images │ └── mlflow-workshop.png ├── jupyter_requirements.txt ├── notebooks │ ├── data │ │ ├── score_windfarm_data.csv │ │ └── windfarm_data.csv │ ├── images │ │ └── forecast_app.png │ ├── rfr_class.ipynb │ ├── run_sentimental_analysis.ipynb │ ├── run_weather_forecast.ipynb │ └── utils_class.ipynb └── slides │ └── mlflow-model-registry-module.pdf ├── models ├── README.md ├── data │ ├── boston_housing.csv │ └── test_petrol_consumption.csv ├── images │ ├── databricks_ce_download_notebooks.png │ ├── mlflow-models-python-model.png │ ├── mlflow-workshop.png │ └── sentiment_analysis.jpg ├── notebooks │ └── dbc │ │ └── MLflow-Models.dbc └── slides │ └── mlflow-models-module.pdf ├── projects ├── README.md ├── images │ ├── databricks_ce_download_notebooks.png │ └── mlflow-workshop.png ├── notebooks │ └── dbc │ │ └── MLflow-Projects.dbc └── slides │ └── mlflow-projects-module.pdf └── tracking ├── README.md ├── data ├── airbnb-cleaned-mlflow.csv ├── bill_authentication.csv ├── petrol_consumption.csv ├── test_bill_authentication.csv ├── test_petrol_consumption.csv ├── windfarm_data.csv └── wine-quality.csv ├── images ├── databricks_ce_create_mlr.png ├── databricks_ce_download_notebooks.png ├── databricks_ce_import_notebooks.png ├── databricks_ce_loging.png ├── mlflow-workshop.png └── mlflow-workshop1-youtube.png ├── notebooks └── dbc │ └── MLflow-Tracking.dbc └── slides └── mlflow-tracking-module.pdf /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![](images/tmls_2021.png) 2 | ## Toronto Machine Learning Workshop Materials for MLflow 3 | 4 | This workshop has four modules. Each one is located in a separate directory with 5 | it respective README.md file and instructions how to follow the module. 6 | 7 | **Note**: For modules 1-3, you'll need [Databricks Community Edition](https://databricks.com/try-databricks). Please register for this 8 | before coming to class. 9 | 10 | 1. [MLflow Tracking](./tracking/README.md) 11 | 2. [MLFlow Projects](./projects/README.md) 12 | 3. [MLflow Models](./models/README.md) 13 | 4. [Model Registry](./model_registery/README.md) 14 | 15 | Each module, except module 4, has [Databricks Community Edition](https://databricks.com/try-databricks) notebooks in its `dbc` format. 16 | For module 4 (Model Registry), we'll use local host using Jupyter Notebooks. Please follow instructions for module 17 | 4 to setup your local host. To get most out of this workshop, I advice you register for the Databricks Community Edition 18 | (DCE) before the workshop. 19 | 20 | Thank you, and I hope you enjoy the flow! 21 | 22 | Cheers, 23 | 24 | Jules S. Damji 25 | 26 | -------------------------------------------------------------------------------- /images/databricks_ce_create_mlr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/databricks_ce_create_mlr.png -------------------------------------------------------------------------------- /images/databricks_ce_download_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/databricks_ce_download_notebooks.png -------------------------------------------------------------------------------- /images/databricks_ce_import_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/databricks_ce_import_notebooks.png -------------------------------------------------------------------------------- /images/databricks_ce_loging.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/databricks_ce_loging.png -------------------------------------------------------------------------------- /images/mlflow-workshop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/mlflow-workshop.png -------------------------------------------------------------------------------- /images/mlflow-workshop1-youtube.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/mlflow-workshop1-youtube.png -------------------------------------------------------------------------------- /images/tmls_2021.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/tmls_2021.png -------------------------------------------------------------------------------- /images/tmls_sv.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/images/tmls_sv.png -------------------------------------------------------------------------------- /model_registery/README.md: -------------------------------------------------------------------------------- 1 | Managing the Complete Machine Learning Lifecycle with MLflow 2 | ============================================================= 3 | ![](./images/mlflow-workshop.png) 4 | 5 | Module 4 of 4 6 | ------------- 7 | Other parts: 8 | - [Module 2](../projects/README.md) 9 | - [Module 3](../models/README.md) 10 | - [Module 4](../model_registery/README.md) 11 | 12 | Content for the MLflow Workshop Series 13 | --------------------------------------- 14 | Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. 15 | 16 | To solve these challenges, [MLflow](https://mlflow.org), an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, 17 | encapsulate models that can be used with many existing tools, and central respositry to share models, 18 | accelerating the ML lifecycle for organizations of any size. 19 | 20 | Goal and Objective 21 | ------------------ 22 | Aimed at beginner or intermediate level, this four-part modules aims to educate data scientists or ML developer in how you 23 | leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing. 24 | 25 | What you will learn 26 | ------------------- 27 | Understand the four main components of open source MLflow——MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each compopnent helps address challenges of the ML lifecycle. 28 | * How to use [MLflow Tracking](https://mlflow.org/docs/latest/tracking.html) to record and query experiments: code, data, config, and results. 29 | * How to use [MLflow Projects](https://mlflow.org/docs/latest/projects.html) packaging format to reproduce runs 30 | * How to use [MLflow Models](https://mlflow.org/docs/latest/models.html) general format to send models to diverse deployment tools. 31 | * How to use [Model Registry](https://mlflow.org/docs/latest/model-registry.html) for collaborative model lifecycle management 32 | * How to use [MLflow UI](https://mlflow.org/docs/latest/tracking.html#tracking-ui) to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics 33 | 34 | 35 | Instructor 36 | ----------- 37 | 38 | - [Jules S. Damji](https://www.linkedin.com/in/dmatrix/) [@2twitme](https://twitter.com/2twitme) 39 | --- 40 | 41 | 42 | About the MLflow Model Registry module 4 43 | ---------------------------------------- 44 | In this module 4, we will cover: 45 | 46 | * Concepts and motivation behind and Model Registry 47 | * Tour of the Model Registry API Documentation 48 | * Understand Model Registry Workflow 49 | * UI Workflow 50 | * API Workflow 51 | * How to create models and register them 52 | * How to use Pyfunc Model Flavor to load models from Model Registry 53 | * Use the Model Registry UI on Jupyter Lab (local host) 54 | 55 | Prerequisites 56 | ------------- 57 | * Python 3, latest pip or pip3, and conda pre installed 58 | * Knowledge on how to use conda 59 | * Knowledge of Python 3 and programming in general 60 | * Preferably a UNIX-based, fully-charged laptop with 8-16 GB, with a Chrome or Firefox browser 61 | * Familiarity with GitHub, git, and an account on Github 62 | * Some knowledge of Machine Learning concepts, libraries, and frameworks 63 | * scikit-learn 64 | * pandas and Numpy 65 | * Loads of virtual laughter, curiosity, and a sense of humor ... :-) 66 | 67 | How to get the Workshop Material 68 | --------------------------------- 69 | 70 | 1. Familiarity with git is important so that you can get all the material easily during 71 | the tutorial and workshop as well as continue to work on in your free time, after the 72 | session is over. 73 | 74 | ```git clone git@github.com:dmatrix/tmls-workshop.git or git clone https://github.com/dmatrix/tmls-workshop.git``` 75 | 76 | Set up your conda environment for Jupyter Lab and MLflow 77 | ------------------------------------------------------- 78 | 79 | 1. `cd tmls-workshop/model_registry` 80 | 2. `conda create --name tmls-jupyter-mlflow` 81 | 3. `conda activate tmls-jupyter-mlflow` 82 | 4. `pip [pip3] install -r jupyter_requirements.txt` 83 | 5. `python -m ipykernel install --user --name=mlflow` 84 | 6. Run `mlflow --help` to check that MLflow's was correctly installed 85 | 7. `mlflow --version` 86 | 8. cd `cd notebooks` 87 | 9. `jupyter lab &` 88 | 10. Open the `run_weather_forecast.ipynb` notebook 89 | 90 | Just flow with MLflow! 91 | -------------------------------------------------------------------------------- /model_registery/images/mlflow-workshop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/model_registery/images/mlflow-workshop.png -------------------------------------------------------------------------------- /model_registery/jupyter_requirements.txt: -------------------------------------------------------------------------------- 1 | mlflow 2 | pandas 3 | numpy 4 | matplotlib 5 | scikit-learn 6 | ipykernel 7 | jupyterlab 8 | -------------------------------------------------------------------------------- /model_registery/notebooks/data/score_windfarm_data.csv: -------------------------------------------------------------------------------- 1 | ,temperature_00,wind_direction_00,wind_speed_00,temperature_08,wind_direction_08,wind_speed_08,temperature_16,wind_direction_16,wind_speed_16,power 2 | 2020-12-27,7.123225402832023,103.17663,8.133746,6.454002189636235,107.79322,6.326991,7.219884363810208,119.070526,3.062219,2621.476 3 | 2020-12-28,5.376270421346036,118.08433,5.558247,8.11883894602458,116.193535,8.565966,9.30717620849609,120.26443,11.993913,5423.625 4 | 2020-12-29,8.593436050415027,115.43259,12.18185,8.587968126932784,112.93136,11.970859,8.956770960489905,110.161095,11.301485,9132.115 5 | 2020-12-30,8.06903266906737,103.169685,9.983466,7.930485343933108,106.04551,6.3815556,8.228901418050114,111.60216,4.0873585,3667.9927 6 | -------------------------------------------------------------------------------- /model_registery/notebooks/images/forecast_app.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/model_registery/notebooks/images/forecast_app.png -------------------------------------------------------------------------------- /model_registery/notebooks/rfr_class.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "application/vnd.databricks.v1+cell": { 8 | "inputWidgets": {}, 9 | "nuid": "38e3d33e-1244-43c4-a563-87d692b8be4d", 10 | "showTitle": false, 11 | "title": "" 12 | } 13 | }, 14 | "outputs": [], 15 | "source": [ 16 | "import warnings\n", 17 | "import mlflow.sklearn\n", 18 | "import numpy as np\n", 19 | "from sklearn.ensemble import RandomForestRegressor\n", 20 | "from sklearn.metrics import mean_squared_error\n", 21 | "from mlflow.models.signature import infer_signature\n", 22 | "\n", 23 | "warnings.filterwarnings(\"ignore\")" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": null, 29 | "metadata": { 30 | "application/vnd.databricks.v1+cell": { 31 | "inputWidgets": {}, 32 | "nuid": "7fb4e44c-365e-4cd3-b4b3-4fa114e55843", 33 | "showTitle": false, 34 | "title": "" 35 | } 36 | }, 37 | "outputs": [], 38 | "source": [ 39 | "class RFRModel():\n", 40 | " def __init__(self, params={}):\n", 41 | " self.rf = RandomForestRegressor(**params)\n", 42 | " self.params = params\n", 43 | " self._mse = None\n", 44 | " self._rsme = None\n", 45 | "\n", 46 | " @classmethod\n", 47 | " def new_instance(cls, params={}):\n", 48 | " return cls(params)\n", 49 | "\n", 50 | " @property\n", 51 | " def model(self):\n", 52 | " return self.rf\n", 53 | "\n", 54 | " @property\n", 55 | " def mse(self):\n", 56 | " return self._mse\n", 57 | "\n", 58 | " @mse.setter\n", 59 | " def mse(self, value):\n", 60 | " self._mse = value\n", 61 | "\n", 62 | " @property\n", 63 | " def rsme(self):\n", 64 | " return self._rsme\n", 65 | "\n", 66 | " @rsme.setter\n", 67 | " def rsme(self, value):\n", 68 | " self._rsme = value\n", 69 | "\n", 70 | " def mlflow_run(self, X_train, y_train, val_x, val_y, model_name,\n", 71 | " run_name=\"Random Forest Regressor: Power Forecasting Model\",\n", 72 | " register=False, verbose=False):\n", 73 | " with mlflow.start_run(run_name=run_name) as run:\n", 74 | " # Log all parameters\n", 75 | " mlflow.log_params(self.params)\n", 76 | "\n", 77 | " # Train and fit the model\n", 78 | " self.rf.fit(X_train, y_train)\n", 79 | " y_pred = self.rf.predict(val_x)\n", 80 | " \n", 81 | " # get the model signature\n", 82 | " model_signature = infer_signature(X_train, y_pred)\n", 83 | "\n", 84 | " # Compute metrics\n", 85 | " self._mse = mean_squared_error(y_pred, val_y)\n", 86 | " self._rsme = np.sqrt(self._mse)\n", 87 | "\n", 88 | " if verbose:\n", 89 | " print(\"Validation MSE: %d\" % self._mse)\n", 90 | " print(\"Validation RMSE: %d\" % self._rsme)\n", 91 | "\n", 92 | " # log params and metrics\n", 93 | " mlflow.log_params(self.params)\n", 94 | " mlflow.log_metric(\"mse\", self._mse)\n", 95 | " mlflow.log_metric(\"rmse\", self._rsme)\n", 96 | "\n", 97 | " # Specify the `registered_model_name` parameter of the\n", 98 | " # function to register the model with the Model Registry. This automatically\n", 99 | " # creates a new model version for each new run\n", 100 | " mlflow.sklearn.log_model(\n", 101 | " sk_model=self.model,\n", 102 | " artifact_path=\"sklearn-model\",\n", 103 | " signature = model_signature,\n", 104 | " registered_model_name=model_name) if register else mlflow.sklearn.log_model(\n", 105 | " sk_model=self.model,\n", 106 | " artifact_path=\"sklearn-model\")\n", 107 | "\n", 108 | " run_id = run.info.run_id\n", 109 | "\n", 110 | " return run_id" 111 | ] 112 | } 113 | ], 114 | "metadata": { 115 | "application/vnd.databricks.v1+notebook": { 116 | "dashboards": [], 117 | "language": "python", 118 | "notebookMetadata": { 119 | "pythonIndentUnit": 2 120 | }, 121 | "notebookName": "rfr_class", 122 | "notebookOrigID": 9778518, 123 | "widgets": {} 124 | }, 125 | "kernelspec": { 126 | "display_name": "Python 3", 127 | "language": "python", 128 | "name": "python3" 129 | }, 130 | "language_info": { 131 | "codemirror_mode": { 132 | "name": "ipython", 133 | "version": 3 134 | }, 135 | "file_extension": ".py", 136 | "mimetype": "text/x-python", 137 | "name": "python", 138 | "nbconvert_exporter": "python", 139 | "pygments_lexer": "ipython3", 140 | "version": "3.7.4" 141 | } 142 | }, 143 | "nbformat": 4, 144 | "nbformat_minor": 4 145 | } 146 | -------------------------------------------------------------------------------- /model_registery/notebooks/run_sentimental_analysis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "application/vnd.databricks.v1+cell": { 7 | "inputWidgets": {}, 8 | "nuid": "7e4ae92f-1707-4e47-aac6-2c809017178a", 9 | "showTitle": false, 10 | "title": "" 11 | } 12 | }, 13 | "source": [ 14 | "#### Problem Tutorial 1: Sentiment Analysis Model\n", 15 | "\n", 16 | "We want to do sentiment analysis by using [VaderSentiment ML framework](https://medium.com/analytics-vidhya/simplifying-social-media-sentiment-analysis-using-vader-in-python-f9e6ec6fc52f) not supported as an MLflow Flavor.\n", 17 | "The goal of sentiment analysis is to \"gauge the attitude, sentiments, evaluations, attitudes and emotions of a speaker/writer based on the computational treatment of subjectivity in a text.\"\n", 18 | "\n", 19 | "VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.\n", 20 | "\n", 21 | "VADER has a lot of advantages over traditional methods of Sentiment Analysis, including:\n", 22 | "\n", 23 | "\n", 24 | " * It works exceedingly well on social media type text, yet readily generalizes to multiple domains\n", 25 | " * It doesn’t require any training data but is constructed from a generalizable, valence-based, human-curated gold standard sentiment lexicon\n", 26 | " * It is fast enough to be used online with streaming data, and\n", 27 | " * It does not severely suffer from a speed-performance tradeoff.\n", 28 | "\n", 29 | "\n", 30 | "\n", 31 | " \n", 35 | "
\n", 32 | " \"Sentiment\n", 34 | "
\n", 36 | "\n", 37 | "[image source](https://medium.com/analytics-vidhya/sentiment-analysis-with-vader-label-the-unlabeled-data-8dd785225166)" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 9, 43 | "metadata": { 44 | "application/vnd.databricks.v1+cell": { 45 | "inputWidgets": {}, 46 | "nuid": "5da195fc-52b9-499d-aa7f-1ecf85445ff3", 47 | "showTitle": false, 48 | "title": "" 49 | } 50 | }, 51 | "outputs": [ 52 | { 53 | "name": "stdout", 54 | "output_type": "stream", 55 | "text": [ 56 | "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.8/site-packages (3.3.2)\n", 57 | "Requirement already satisfied: requests in /usr/local/lib/python3.8/site-packages (from vaderSentiment) (2.25.0)\n", 58 | "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/site-packages (from requests->vaderSentiment) (2.10)\n", 59 | "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/site-packages (from requests->vaderSentiment) (3.0.4)\n", 60 | "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/site-packages (from requests->vaderSentiment) (2020.12.5)\n", 61 | "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/site-packages (from requests->vaderSentiment) (1.26.2)\n", 62 | "Note: you may need to restart the kernel to use updated packages.\n" 63 | ] 64 | } 65 | ], 66 | "source": [ 67 | "%pip install vaderSentiment" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": { 73 | "application/vnd.databricks.v1+cell": { 74 | "inputWidgets": {}, 75 | "nuid": "fd1be150-ced2-4763-b99e-4d261e60f112", 76 | "showTitle": false, 77 | "title": "" 78 | } 79 | }, 80 | "source": [ 81 | "### VaderSentiment Python Package\n", 82 | "\n", 83 | "You can read the orignal paper by authors [here](http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf)." 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 10, 89 | "metadata": { 90 | "application/vnd.databricks.v1+cell": { 91 | "inputWidgets": {}, 92 | "nuid": "e084d0ba-81da-4927-8300-be1fa4469fa6", 93 | "showTitle": false, 94 | "title": "" 95 | } 96 | }, 97 | "outputs": [], 98 | "source": [ 99 | "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n", 100 | "import pandas as pd\n", 101 | "import mlflow.pyfunc" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 11, 107 | "metadata": { 108 | "application/vnd.databricks.v1+cell": { 109 | "inputWidgets": {}, 110 | "nuid": "5baf96c4-5e00-45e9-a036-53dc9fcafd9d", 111 | "showTitle": false, 112 | "title": "" 113 | } 114 | }, 115 | "outputs": [], 116 | "source": [ 117 | "# Define some input text\n", 118 | "\n", 119 | "INPUT_TEXTS = [{'text': \"This is a bad ass movie. You got to see it! :-)\"},\n", 120 | " {'text': \"Ricky Gervais is smart, witty, and creative!!!!!! :D\"},\n", 121 | " {'text': \"LOL, this guy fell off a chair while sleeping and snoring in a meeting\"},\n", 122 | " {'text': \"Men shoots himself while trying to steal a dog, OMG\"},\n", 123 | " {'text': \"Yay!! Another good phone interview. I nailed it!!\"},\n", 124 | " {'text': \"This is INSANE! I can't believe it. How could you do such a horrible thing?\"}]" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": { 130 | "application/vnd.databricks.v1+cell": { 131 | "inputWidgets": {}, 132 | "nuid": "2d8b58df-0363-46e1-8c87-18ddee19d166", 133 | "showTitle": false, 134 | "title": "" 135 | } 136 | }, 137 | "source": [ 138 | "### Define a SocialMediaAnalyserModel\n", 139 | "\n", 140 | "This is a subclass of [PythonModel](https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.PythonModel)" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 12, 146 | "metadata": { 147 | "application/vnd.databricks.v1+cell": { 148 | "inputWidgets": {}, 149 | "nuid": "7c9475c2-b8ce-4d55-aee3-d72c6144fdcc", 150 | "showTitle": false, 151 | "title": "" 152 | } 153 | }, 154 | "outputs": [], 155 | "source": [ 156 | "class SocialMediaAnalyserModel(mlflow.pyfunc.PythonModel):\n", 157 | "\n", 158 | " def __init__(self):\n", 159 | " \"\"\"\n", 160 | " Constructor for our Cusomized PyFunc PythonModel Class\n", 161 | " \"\"\"\n", 162 | " super().__init__()\n", 163 | " # Initialize an instance of vader analyser\n", 164 | " self._analyser = SentimentIntensityAnalyzer()\n", 165 | "\n", 166 | " def _score(self, text):\n", 167 | " \"\"\"\n", 168 | " Private function to analyse the scores. It invokes model's polarity_scores \n", 169 | " param: text to analyse\n", 170 | " return: sentiment analyses scores\n", 171 | " \"\"\"\n", 172 | " scores = self._analyser.polarity_scores(text)\n", 173 | " return scores\n", 174 | "\n", 175 | " def predict(self, context, model_input):\n", 176 | " \"\"\"\n", 177 | " Implement the predict function required for PythonModel\n", 178 | " \"\"\"\n", 179 | " model_output = model_input.apply(lambda col: self._score(col))\n", 180 | " return model_output" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 13, 186 | "metadata": { 187 | "application/vnd.databricks.v1+cell": { 188 | "inputWidgets": {}, 189 | "nuid": "3d2c3a20-847e-4804-8208-313f47ad048a", 190 | "showTitle": false, 191 | "title": "" 192 | } 193 | }, 194 | "outputs": [], 195 | "source": [ 196 | "def mlflow_run():\n", 197 | " \n", 198 | " # Save the conda environment for this model. \n", 199 | " conda_env = {\n", 200 | " 'channels': ['defaults', 'conda-forge'],\n", 201 | " 'dependencies': [\n", 202 | " 'python=3.7.6',\n", 203 | " 'pip'],\n", 204 | " 'pip': [\n", 205 | " 'mlflow',\n", 206 | " 'cloudpickle==1.3.0',\n", 207 | " 'vaderSentiment==3.3.2'\n", 208 | " ],\n", 209 | " 'name': 'mlflow-env'\n", 210 | " }\n", 211 | " \n", 212 | " mlflow.set_tracking_uri(\"sqlite:///mlruns.db\")\n", 213 | "\n", 214 | " # Model name and create an instance of PyFuncModel\n", 215 | " model_path = \"vader\"\n", 216 | " vader_model = SocialMediaAnalyserModel()\n", 217 | " with mlflow.start_run(run_name=\"Vader Sentiment Analysis\") as run:\n", 218 | " # Log MLflow entities: params and model\n", 219 | " mlflow.pyfunc.log_model(model_path, python_model=vader_model, conda_env=conda_env, registered_model_name=\"PyFuncVader\")\n", 220 | " mlflow.log_param(\"algorithm\", \"VADER\")\n", 221 | " mlflow.log_param(\"total_sentiments\", len(INPUT_TEXTS))" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 14, 227 | "metadata": { 228 | "application/vnd.databricks.v1+cell": { 229 | "inputWidgets": {}, 230 | "nuid": "9de8b40d-24e6-42ba-abba-7defbb57d864", 231 | "showTitle": false, 232 | "title": "" 233 | } 234 | }, 235 | "outputs": [ 236 | { 237 | "name": "stderr", 238 | "output_type": "stream", 239 | "text": [ 240 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 241 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n", 242 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 243 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n", 244 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 245 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n", 246 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 247 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n", 248 | "Registered model 'PyFuncVader' already exists. Creating a new version of this model...\n", 249 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 250 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n", 251 | "2021/06/07 09:24:03 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation. Model name: PyFuncVader, version 2\n", 252 | "Created version '2' of model 'PyFuncVader'.\n", 253 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 254 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n", 255 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 256 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n", 257 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 258 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n" 259 | ] 260 | } 261 | ], 262 | "source": [ 263 | "mlflow_run()" 264 | ] 265 | }, 266 | { 267 | "cell_type": "markdown", 268 | "metadata": { 269 | "application/vnd.databricks.v1+cell": { 270 | "inputWidgets": {}, 271 | "nuid": "67cb98ee-cf36-4bae-92bf-ebe551474737", 272 | "showTitle": false, 273 | "title": "" 274 | } 275 | }, 276 | "source": [ 277 | "### Load as a pyfunc_model from the model registry" 278 | ] 279 | }, 280 | { 281 | "cell_type": "code", 282 | "execution_count": 15, 283 | "metadata": { 284 | "application/vnd.databricks.v1+cell": { 285 | "inputWidgets": {}, 286 | "nuid": "d8f8440c-80a6-4fd1-af2a-a334cb36effa", 287 | "showTitle": false, 288 | "title": "" 289 | } 290 | }, 291 | "outputs": [ 292 | { 293 | "name": "stderr", 294 | "output_type": "stream", 295 | "text": [ 296 | "INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n", 297 | "INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n" 298 | ] 299 | }, 300 | { 301 | "name": "stdout", 302 | "output_type": "stream", 303 | "text": [ 304 | " --> {'neg': 0.0, 'neu': 0.527, 'pos': 0.473, 'compound': 0.6696}>\n", 305 | " --> {'neg': 0.262, 'neu': 0.738, 'pos': 0.0, 'compound': -0.4939}>\n", 306 | " --> {'neg': 0.0, 'neu': 0.446, 'pos': 0.554, 'compound': 0.816}>\n" 307 | ] 308 | } 309 | ], 310 | "source": [ 311 | "# Load back the model as a pyfunc_model for scoring\n", 312 | "input_texts = [\"Got to love this code snippet!\", \n", 313 | " \"Men shoots himself while trying to steal a dog, OMG\", \n", 314 | " \"Yay!! Another good phone interview. I nailed it!!\"]\n", 315 | "\n", 316 | "model_uri = f\"models:/PyFuncVader/1\"\n", 317 | "pyfunc_model = mlflow.pyfunc.load_model(model_uri)\n", 318 | "for i, text in enumerate(input_texts):\n", 319 | " score = pyfunc_model.predict(pd.DataFrame([text]))\n", 320 | " print(f\"<{text}> --> {str(score[0])}>\")" 321 | ] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": 8, 326 | "metadata": { 327 | "application/vnd.databricks.v1+cell": { 328 | "inputWidgets": {}, 329 | "nuid": "ccb7f7a0-c811-4305-bc5b-998ce2a4278c", 330 | "showTitle": false, 331 | "title": "" 332 | } 333 | }, 334 | "outputs": [ 335 | { 336 | "data": { 337 | "text/plain": [ 338 | "'[{\"0\":\"Got to love this code snippet!\",\"1\":\"Men shoots himself while trying to steal a dog, OMG\",\"2\":\"Yay!! Another good phone interview. I nailed it!!\"}]'" 339 | ] 340 | }, 341 | "execution_count": 8, 342 | "metadata": {}, 343 | "output_type": "execute_result" 344 | } 345 | ], 346 | "source": [ 347 | "model_input = pd.DataFrame([[\"Got to love this code snippet!\", \"Men shoots himself while trying to steal a dog, OMG\", \"Yay!! Another good phone interview. I nailed it!!\"]])\n", 348 | "pd.DataFrame.to_json(model_input, orient='records')" 349 | ] 350 | } 351 | ], 352 | "metadata": { 353 | "application/vnd.databricks.v1+notebook": { 354 | "dashboards": [], 355 | "language": "python", 356 | "notebookMetadata": { 357 | "experimentId": "9778498", 358 | "pythonIndentUnit": 2 359 | }, 360 | "notebookName": "SentimentAnalysis", 361 | "notebookOrigID": 9778498, 362 | "widgets": {} 363 | }, 364 | "kernelspec": { 365 | "display_name": "Python 3", 366 | "language": "python", 367 | "name": "python3" 368 | }, 369 | "language_info": { 370 | "codemirror_mode": { 371 | "name": "ipython", 372 | "version": 3 373 | }, 374 | "file_extension": ".py", 375 | "mimetype": "text/x-python", 376 | "name": "python", 377 | "nbconvert_exporter": "python", 378 | "pygments_lexer": "ipython3", 379 | "version": "3.8.10" 380 | } 381 | }, 382 | "nbformat": 4, 383 | "nbformat_minor": 4 384 | } 385 | -------------------------------------------------------------------------------- /model_registery/notebooks/utils_class.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"cell_type":"code","source":["import pandas as pd\nimport tempfile\nimport warnings\nwarnings.filterwarnings(\"ignore\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"544346fb-2f8d-4ce7-a56e-54f5b2712144"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["class Utils:\n @staticmethod\n def load_data(path, index_col=0):\n df = pd.read_csv(path, index_col=0)\n return df\n\n @staticmethod\n def get_training_data(df):\n training_data = pd.DataFrame(df[\"2014-01-01\":\"2018-01-01\"])\n X = training_data.drop(columns=\"power\")\n y = training_data[\"power\"]\n return X, y\n\n @staticmethod\n def get_validation_data(df):\n validation_data = pd.DataFrame(df[\"2018-01-01\":\"2019-01-01\"])\n X = validation_data.drop(columns=\"power\")\n y = validation_data[\"power\"]\n return X, y\n\n @staticmethod\n def print_pandas_dataset(d, n=5):\n \"\"\"\n Given a Pandas dataFrame show the dimensions sizes\n :param d: Pandas dataFrame\n :return: None\n \"\"\"\n print(\"rows = %d; columns=%d\" % (d.shape[0], d.shape[1]))\n print(d.head(n))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0ab686a0-878c-4638-88ff-4018295f5fe8"}},"outputs":[],"execution_count":0}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"mimetype":"text/x-python","name":"python","pygments_lexer":"ipython3","codemirror_mode":{"name":"ipython","version":3},"version":"3.9.1","nbconvert_exporter":"python","file_extension":".py"},"application/vnd.databricks.v1+notebook":{"notebookName":"utils_class","dashboards":[],"notebookMetadata":{"pythonIndentUnit":2},"language":"python","widgets":{},"notebookOrigID":9778521}},"nbformat":4,"nbformat_minor":0} 2 | -------------------------------------------------------------------------------- /model_registery/slides/mlflow-model-registry-module.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/model_registery/slides/mlflow-model-registry-module.pdf -------------------------------------------------------------------------------- /models/README.md: -------------------------------------------------------------------------------- 1 | Managing the Complete Machine Learning Lifecycle with MLflow 2 | ============================================================= 3 | ![](./images/mlflow-workshop.png) 4 | 5 | Module 3 of 4 6 | ------------- 7 | Other parts: 8 | - [Module 2](../projects/README.md) 9 | - [Module 3](../models/README.md) 10 | - [Module 4](../model_registery/README.md) 11 | 12 | Content for the MLflow Workshop Series 13 | --------------------------------------- 14 | Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. 15 | 16 | To solve these challenges, [MLflow](https://mlflow.org), an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, 17 | encapsulate models that can be used with many existing tools, and central respositry to share models, 18 | accelerating the ML lifecycle for organizations of any size. 19 | 20 | Goal and Objective 21 | ------------------ 22 | Aimed at beginner or intermediate level, this four-part modules aims to educate data scientists or ML developer in how you 23 | leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing. 24 | 25 | What you will learn 26 | ------------------- 27 | Understand the four main components of open source MLflow——MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each compopnent helps address challenges of the ML lifecycle. 28 | * How to use [MLflow Tracking](https://mlflow.org/docs/latest/tracking.html) to record and query experiments: code, data, config, and results. 29 | * How to use [MLflow Projects](https://mlflow.org/docs/latest/projects.html) packaging format to reproduce runs 30 | * How to use [MLflow Models](https://mlflow.org/docs/latest/models.html) general format to send models to diverse deployment tools. 31 | * How to use [Model Registry](https://mlflow.org/docs/latest/model-registry.html) for collaborative model lifecycle management 32 | * How to use [MLflow UI](https://mlflow.org/docs/latest/tracking.html#tracking-ui) to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics 33 | 34 | 35 | Instructor 36 | ----------- 37 | 38 | - [Jules S. Damji](https://www.linkedin.com/in/dmatrix/) [@2twitme](https://twitter.com/2twitme) 39 | --- 40 | Concepts and motivation behind and Models 41 | Tour of the the MLflow Model API Documentation 42 | How to create different model flavors 43 | Pyfunc Model Flavor: what and how to use it 44 | 45 | 46 | About the MLflow Models module 3 47 | -------------------------------- 48 | 49 | In this module 3, we will cover: 50 | * Concepts and motivation behind and Models 51 | * Tour of the MLflow Model API Documentation 52 | * How to create different model flavors 53 | * Pyfunc Model Flavor: what and how to use it 54 | * Use the MLflow UI on the DCE 55 | 56 | Prerequisites 57 | ------------- 58 | * Before the session, please pre-register for [Databricks Community Edition](https://databricks.com/try-databricks) 59 | * Knowledge of Python 3 and programming in general 60 | * Preferably a UNIX-based, fully-charged laptop with 8-16 GB, with a Chrome or Firefox browser 61 | * Familiarity with GitHub, git, and an account on Github 62 | * Some knowledge of Machine Learning concepts, libraries, and frameworks 63 | * scikit-learn 64 | * pandas and Numpy 65 | * matplotlib 66 | * Keras/TensorFlow 67 | * [**optional for module-1**] PyCharm/IntelliJ or choice of syntax-based Python editor 68 | * [**optional for module-1**] pip/pip3 or conda and Python 3 installed 69 | * Loads of virtual laughter, curiosity, and a sense of humor ... :-) 70 | 71 | Obtaining the Tutorial Material 72 | -------------------------------- 73 | 74 | Familiarity with **git** is important so that you can get all the material easily during the tutorial and 75 | workshop as well as continue to work in your free time, after the session is over. 76 | 77 | ``` git clone github.com:dmatrix/tmls-workshop.git or git clone https://github.com/dmatrix/tmls-workshop.git``` 78 | 79 | Documentation Resources 80 | ----------------------- 81 | 82 | This tutorial will refer to documentation: 83 | 84 | 1. [MLflow](https://mlflow.org/docs/latest/index.html) 85 | 2. [Numpy](https://numpy.org/devdocs/user/quickstart.html) 86 | 3. [Pandas](https://pandas.pydata.org/pandas-docs/stable/reference/index.html) 87 | 4. [Scikit-Learn](https://scikit-learn.org/stable/index.html) 88 | 5. [Keras](https://keras.io/optimizers/) 89 | 6. [TensorFlow](https://tensorflow.org) 90 | 7. [Matplotlib](https://matplotlib.org/3.2.0/tutorials/introductory/pyplot.html) 91 | 92 | How to get started 93 | ------------------- 94 | We will walk through this during the session, but please sign up for [Databricks Community Edition](https://databricks.com/try-databricks) before the session : 95 | 96 | 1. ```git clone github.com:dmatrix/tmls-workshop.git or git clone https://github.com/dmatrix/tmls-workshop.git``` 97 | 2. Use this [URL](https://community.cloud.databricks.com/login.html) to log into the Databricks Community Edition 98 | 99 | ![](../images/databricks_ce_loging.png) 100 | 101 | 3. Create a **8.2 ML (includes Apache Spark 3.1.1, Scala 2.12)** cluster 102 | 103 | ![](../images/databricks_ce_create_mlr.png) 104 | 105 | 4. In the browser: 106 | * (1) Go the GitHub **projects/notebooks/dbc/** subdirectory 107 | * (2) Download **MLflow-Models.dbc** file on your laptop 108 | 109 | 5. Import the **MLflow-Projects.dbc** file into the Databricks Community Edition 110 | 111 | ![](../images/databricks_ce_import_notebooks.png) 112 | 113 | Let's go! 114 | 115 | Cheers, 116 | 117 | Jules 118 | 119 | 120 | -------------------------------------------------------------------------------- /models/data/boston_housing.csv: -------------------------------------------------------------------------------- 1 | "crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat","medv" 2 | 0.00632,18,2.31,"0",0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24 3 | 0.02731,0,7.07,"0",0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6 4 | 0.02729,0,7.07,"0",0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7 5 | 0.03237,0,2.18,"0",0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4 6 | 0.06905,0,2.18,"0",0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2 7 | 0.02985,0,2.18,"0",0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7 8 | 0.08829,12.5,7.87,"0",0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9 9 | 0.14455,12.5,7.87,"0",0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1 10 | 0.21124,12.5,7.87,"0",0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5 11 | 0.17004,12.5,7.87,"0",0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9 12 | 0.22489,12.5,7.87,"0",0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15 13 | 0.11747,12.5,7.87,"0",0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9 14 | 0.09378,12.5,7.87,"0",0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7 15 | 0.62976,0,8.14,"0",0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4 16 | 0.63796,0,8.14,"0",0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2 17 | 0.62739,0,8.14,"0",0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9 18 | 1.05393,0,8.14,"0",0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1 19 | 0.7842,0,8.14,"0",0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5 20 | 0.80271,0,8.14,"0",0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2 21 | 0.7258,0,8.14,"0",0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2 22 | 1.25179,0,8.14,"0",0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6 23 | 0.85204,0,8.14,"0",0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6 24 | 1.23247,0,8.14,"0",0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2 25 | 0.98843,0,8.14,"0",0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5 26 | 0.75026,0,8.14,"0",0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6 27 | 0.84054,0,8.14,"0",0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9 28 | 0.67191,0,8.14,"0",0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6 29 | 0.95577,0,8.14,"0",0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8 30 | 0.77299,0,8.14,"0",0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4 31 | 1.00245,0,8.14,"0",0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21 32 | 1.13081,0,8.14,"0",0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7 33 | 1.35472,0,8.14,"0",0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5 34 | 1.38799,0,8.14,"0",0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2 35 | 1.15172,0,8.14,"0",0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1 36 | 1.61282,0,8.14,"0",0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5 37 | 0.06417,0,5.96,"0",0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9 38 | 0.09744,0,5.96,"0",0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20 39 | 0.08014,0,5.96,"0",0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21 40 | 0.17505,0,5.96,"0",0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7 41 | 0.02763,75,2.95,"0",0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8 42 | 0.03359,75,2.95,"0",0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9 43 | 0.12744,0,6.91,"0",0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6 44 | 0.1415,0,6.91,"0",0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3 45 | 0.15936,0,6.91,"0",0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7 46 | 0.12269,0,6.91,"0",0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2 47 | 0.17142,0,6.91,"0",0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21,19.3 48 | 0.18836,0,6.91,"0",0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15,20 49 | 0.22927,0,6.91,"0",0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8,16.6 50 | 0.25387,0,6.91,"0",0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81,14.4 51 | 0.21977,0,6.91,"0",0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2,19.4 52 | 0.08873,21,5.64,"0",0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45,19.7 53 | 0.04337,21,5.64,"0",0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43,20.5 54 | 0.0536,21,5.64,"0",0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28,25 55 | 0.04981,21,5.64,"0",0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43,23.4 56 | 0.0136,75,4,"0",0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8,18.9 57 | 0.01311,90,1.22,"0",0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81,35.4 58 | 0.02055,85,0.74,"0",0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77,24.7 59 | 0.01432,100,1.32,"0",0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95,31.6 60 | 0.15445,25,5.13,"0",0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86,23.3 61 | 0.10328,25,5.13,"0",0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22,19.6 62 | 0.14932,25,5.13,"0",0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15,18.7 63 | 0.17171,25,5.13,"0",0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44,16 64 | 0.11027,25,5.13,"0",0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73,22.2 65 | 0.1265,25,5.13,"0",0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5,25 66 | 0.01951,17.5,1.38,"0",0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05,33 67 | 0.03584,80,3.37,"0",0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67,23.5 68 | 0.04379,80,3.37,"0",0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24,19.4 69 | 0.05789,12.5,6.07,"0",0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1,22 70 | 0.13554,12.5,6.07,"0",0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09,17.4 71 | 0.12816,12.5,6.07,"0",0.409,5.885,33,6.498,4,345,18.9,396.9,8.79,20.9 72 | 0.08826,0,10.81,"0",0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72,24.2 73 | 0.15876,0,10.81,"0",0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88,21.7 74 | 0.09164,0,10.81,"0",0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52,22.8 75 | 0.19539,0,10.81,"0",0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54,23.4 76 | 0.07896,0,12.83,"0",0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78,24.1 77 | 0.09512,0,12.83,"0",0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94,21.4 78 | 0.10153,0,12.83,"0",0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97,20 79 | 0.08707,0,12.83,"0",0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27,20.8 80 | 0.05646,0,12.83,"0",0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34,21.2 81 | 0.08387,0,12.83,"0",0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1,20.3 82 | 0.04113,25,4.86,"0",0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29,28 83 | 0.04462,25,4.86,"0",0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22,23.9 84 | 0.03659,25,4.86,"0",0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72,24.8 85 | 0.03551,25,4.86,"0",0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51,22.9 86 | 0.05059,0,4.49,"0",0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62,23.9 87 | 0.05735,0,4.49,"0",0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53,26.6 88 | 0.05188,0,4.49,"0",0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86,22.5 89 | 0.07151,0,4.49,"0",0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44,22.2 90 | 0.0566,0,3.41,"0",0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5,23.6 91 | 0.05302,0,3.41,"0",0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7,28.7 92 | 0.04684,0,3.41,"0",0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81,22.6 93 | 0.03932,0,3.41,"0",0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2,22 94 | 0.04203,28,15.04,"0",0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16,22.9 95 | 0.02875,28,15.04,"0",0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21,25 96 | 0.04294,28,15.04,"0",0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59,20.6 97 | 0.12204,0,2.89,"0",0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65,28.4 98 | 0.11504,0,2.89,"0",0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34,21.4 99 | 0.12083,0,2.89,"0",0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7 100 | 0.08187,0,2.89,"0",0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57,43.8 101 | 0.0686,0,2.89,"0",0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19,33.2 102 | 0.14866,0,8.56,"0",0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42,27.5 103 | 0.11432,0,8.56,"0",0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5 104 | 0.22876,0,8.56,"0",0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63,18.6 105 | 0.21161,0,8.56,"0",0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44,19.3 106 | 0.1396,0,8.56,"0",0.52,6.167,90,2.421,5,384,20.9,392.69,12.33,20.1 107 | 0.13262,0,8.56,"0",0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47,19.5 108 | 0.1712,0,8.56,"0",0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66,19.5 109 | 0.13117,0,8.56,"0",0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09,20.4 110 | 0.12802,0,8.56,"0",0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27,19.8 111 | 0.26363,0,8.56,"0",0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55,19.4 112 | 0.10793,0,8.56,"0",0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13,21.7 113 | 0.10084,0,10.01,"0",0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16,22.8 114 | 0.12329,0,10.01,"0",0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21,18.8 115 | 0.22212,0,10.01,"0",0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09,18.7 116 | 0.14231,0,10.01,"0",0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45,18.5 117 | 0.17134,0,10.01,"0",0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76,18.3 118 | 0.13158,0,10.01,"0",0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04,21.2 119 | 0.15098,0,10.01,"0",0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2 120 | 0.13058,0,10.01,"0",0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4 121 | 0.14476,0,10.01,"0",0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61,19.3 122 | 0.06899,0,25.65,"0",0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37,22 123 | 0.07165,0,25.65,"0",0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3 124 | 0.09299,0,25.65,"0",0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93,20.5 125 | 0.15038,0,25.65,"0",0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3 126 | 0.09849,0,25.65,"0",0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8 127 | 0.16902,0,25.65,"0",0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81,21.4 128 | 0.38735,0,25.65,"0",0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26,15.7 129 | 0.25915,0,21.89,"0",0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2 130 | 0.32543,0,21.89,"0",0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18 131 | 0.88125,0,21.89,"0",0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3 132 | 0.34006,0,21.89,"0",0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2 133 | 1.19294,0,21.89,"0",0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6 134 | 0.59005,0,21.89,"0",0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23 135 | 0.32982,0,21.89,"0",0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4 136 | 0.97617,0,21.89,"0",0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6 137 | 0.55778,0,21.89,"0",0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1 138 | 0.32264,0,21.89,"0",0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4 139 | 0.35233,0,21.89,"0",0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59,17.1 140 | 0.2498,0,21.89,"0",0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3 141 | 0.54452,0,21.89,"0",0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8 142 | 0.2909,0,21.89,"0",0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14 143 | 1.62864,0,21.89,"0",0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4 144 | 3.32105,0,19.58,"1",0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4 145 | 4.0974,0,19.58,"0",0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6 146 | 2.77974,0,19.58,"0",0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8 147 | 2.37934,0,19.58,"0",0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8 148 | 2.15505,0,19.58,"0",0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6 149 | 2.36862,0,19.58,"0",0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6 150 | 2.33099,0,19.58,"0",0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8 151 | 2.73397,0,19.58,"0",0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4 152 | 1.6566,0,19.58,"0",0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5 153 | 1.49632,0,19.58,"0",0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6 154 | 1.12658,0,19.58,"1",0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3 155 | 2.14918,0,19.58,"0",0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4 156 | 1.41385,0,19.58,"1",0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17 157 | 3.53501,0,19.58,"1",0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6 158 | 2.44668,0,19.58,"0",0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1 159 | 1.22358,0,19.58,"0",0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3 160 | 1.34284,0,19.58,"0",0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3 161 | 1.42502,0,19.58,"0",0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3 162 | 1.27346,0,19.58,"1",0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27 163 | 1.46336,0,19.58,"0",0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50 164 | 1.83377,0,19.58,"1",0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50 165 | 1.51902,0,19.58,"1",0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50 166 | 2.24236,0,19.58,"0",0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7 167 | 2.924,0,19.58,"0",0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25 168 | 2.01019,0,19.58,"0",0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50 169 | 1.80028,0,19.58,"0",0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8 170 | 2.3004,0,19.58,"0",0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8 171 | 2.44953,0,19.58,"0",0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3 172 | 1.20742,0,19.58,"0",0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4 173 | 2.3139,0,19.58,"0",0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1 174 | 0.13914,0,4.05,"0",0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1 175 | 0.09178,0,4.05,"0",0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6 176 | 0.08447,0,4.05,"0",0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6 177 | 0.06664,0,4.05,"0",0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4 178 | 0.07022,0,4.05,"0",0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2 179 | 0.05425,0,4.05,"0",0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6 180 | 0.06642,0,4.05,"0",0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9 181 | 0.0578,0,2.46,"0",0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2 182 | 0.06588,0,2.46,"0",0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8 183 | 0.06888,0,2.46,"0",0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2 184 | 0.09103,0,2.46,"0",0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9 185 | 0.10008,0,2.46,"0",0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5 186 | 0.08308,0,2.46,"0",0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4 187 | 0.06047,0,2.46,"0",0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6 188 | 0.05602,0,2.46,"0",0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50 189 | 0.07875,45,3.44,"0",0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32 190 | 0.12579,45,3.44,"0",0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8 191 | 0.0837,45,3.44,"0",0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9 192 | 0.09068,45,3.44,"0",0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37 193 | 0.06911,45,3.44,"0",0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5 194 | 0.08664,45,3.44,"0",0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4 195 | 0.02187,60,2.93,"0",0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1 196 | 0.01439,60,2.93,"0",0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1 197 | 0.01381,80,0.46,"0",0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50 198 | 0.04011,80,1.52,"0",0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3 199 | 0.04666,80,1.52,"0",0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3 200 | 0.03768,80,1.52,"0",0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6 201 | 0.0315,95,1.47,"0",0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9 202 | 0.01778,95,1.47,"0",0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9 203 | 0.03445,82.5,2.03,"0",0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1 204 | 0.02177,82.5,2.03,"0",0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3 205 | 0.0351,95,2.68,"0",0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5 206 | 0.02009,95,2.68,"0",0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50 207 | 0.13642,0,10.59,"0",0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6 208 | 0.22969,0,10.59,"0",0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4 209 | 0.25199,0,10.59,"0",0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06,22.5 210 | 0.13587,0,10.59,"1",0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4 211 | 0.43571,0,10.59,"1",0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20 212 | 0.17446,0,10.59,"1",0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7 213 | 0.37578,0,10.59,"1",0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3 214 | 0.21719,0,10.59,"1",0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4 215 | 0.14052,0,10.59,"0",0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1 216 | 0.28955,0,10.59,"0",0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7 217 | 0.19802,0,10.59,"0",0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47,25 218 | 0.0456,0,13.89,"1",0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3 219 | 0.07013,0,13.89,"0",0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7 220 | 0.11069,0,13.89,"1",0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5 221 | 0.11425,0,13.89,"1",0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23 222 | 0.35809,0,6.2,"1",0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7 223 | 0.40771,0,6.2,"1",0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7 224 | 0.62356,0,6.2,"1",0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5 225 | 0.6147,0,6.2,"0",0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1 226 | 0.31533,0,6.2,"0",0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8 227 | 0.52693,0,6.2,"0",0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50 228 | 0.38214,0,6.2,"0",0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13,37.6 229 | 0.41238,0,6.2,"0",0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6 230 | 0.29819,0,6.2,"0",0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92,46.7 231 | 0.44178,0,6.2,"0",0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5 232 | 0.537,0,6.2,"0",0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3 233 | 0.46296,0,6.2,"0",0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7 234 | 0.57529,0,6.2,"0",0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7 235 | 0.33147,0,6.2,"0",0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95,48.3 236 | 0.44791,0,6.2,"1",0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29 237 | 0.33045,0,6.2,"0",0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24 238 | 0.52058,0,6.2,"1",0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1 239 | 0.51183,0,6.2,"0",0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5 240 | 0.08244,30,4.93,"0",0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7 241 | 0.09252,30,4.93,"0",0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3 242 | 0.11329,30,4.93,"0",0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22 243 | 0.10612,30,4.93,"0",0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1 244 | 0.1029,30,4.93,"0",0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2 245 | 0.12757,30,4.93,"0",0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7 246 | 0.20608,22,5.86,"0",0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6 247 | 0.19133,22,5.86,"0",0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5 248 | 0.33983,22,5.86,"0",0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3 249 | 0.19657,22,5.86,"0",0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5 250 | 0.16439,22,5.86,"0",0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5 251 | 0.19073,22,5.86,"0",0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2 252 | 0.1403,22,5.86,"0",0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4 253 | 0.21409,22,5.86,"0",0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8 254 | 0.08221,22,5.86,"0",0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6 255 | 0.36894,22,5.86,"0",0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8 256 | 0.04819,80,3.64,"0",0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9 257 | 0.03548,80,3.64,"0",0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9 258 | 0.01538,90,3.75,"0",0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44 259 | 0.61154,20,3.97,"0",0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50 260 | 0.66351,20,3.97,"0",0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36 261 | 0.65665,20,3.97,"0",0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1 262 | 0.54011,20,3.97,"0",0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8 263 | 0.53412,20,3.97,"0",0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1 264 | 0.52014,20,3.97,"0",0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8 265 | 0.82526,20,3.97,"0",0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31 266 | 0.55007,20,3.97,"0",0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5 267 | 0.76162,20,3.97,"0",0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8 268 | 0.7857,20,3.97,"0",0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7 269 | 0.57834,20,3.97,"0",0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50 270 | 0.5405,20,3.97,"0",0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5 271 | 0.09065,20,6.96,"1",0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7 272 | 0.29916,20,6.96,"0",0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1 273 | 0.16211,20,6.96,"0",0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59,25.2 274 | 0.1146,20,6.96,"0",0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4 275 | 0.22188,20,6.96,"1",0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2 276 | 0.05644,40,6.41,"1",0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4 277 | 0.09604,40,6.41,"0",0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32 278 | 0.10469,40,6.41,"1",0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2 279 | 0.06127,40,6.41,"1",0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16,33.1 280 | 0.07978,40,6.41,"0",0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1 281 | 0.21038,20,3.33,"0",0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1 282 | 0.03578,20,3.33,"0",0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4 283 | 0.03705,20,3.33,"0",0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59,35.4 284 | 0.06129,20,3.33,"1",0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46 285 | 0.01501,90,1.21,"1",0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50 286 | 0.00906,90,2.97,"0",0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2 287 | 0.01096,55,2.25,"0",0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22 288 | 0.01965,80,1.76,"0",0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93,20.1 289 | 0.03871,52.5,5.32,"0",0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2 290 | 0.0459,52.5,5.32,"0",0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3 291 | 0.04297,52.5,5.32,"0",0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8 292 | 0.03502,80,4.95,"0",0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5 293 | 0.07886,80,4.95,"0",0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3 294 | 0.03615,80,4.95,"0",0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9 295 | 0.08265,0,13.92,"0",0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9 296 | 0.08199,0,13.92,"0",0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7 297 | 0.12932,0,13.92,"0",0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6 298 | 0.05372,0,13.92,"0",0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1 299 | 0.14103,0,13.92,"0",0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3 300 | 0.06466,70,2.24,"0",0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5 301 | 0.05561,70,2.24,"0",0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29 302 | 0.04417,70,2.24,"0",0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8 303 | 0.03537,34,6.09,"0",0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22 304 | 0.09266,34,6.09,"0",0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4 305 | 0.1,34,6.09,"0",0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1 306 | 0.05515,33,2.18,"0",0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1 307 | 0.05479,33,2.18,"0",0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4 308 | 0.07503,33,2.18,"0",0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4 309 | 0.04932,33,2.18,"0",0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2 310 | 0.49298,0,9.9,"0",0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8 311 | 0.3494,0,9.9,"0",0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3 312 | 2.63548,0,9.9,"0",0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1 313 | 0.79041,0,9.9,"0",0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1 314 | 0.26169,0,9.9,"0",0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4 315 | 0.26938,0,9.9,"0",0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6 316 | 0.3692,0,9.9,"0",0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8 317 | 0.25356,0,9.9,"0",0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2 318 | 0.31827,0,9.9,"0",0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33,17.8 319 | 0.24522,0,9.9,"0",0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8 320 | 0.40202,0,9.9,"0",0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1 321 | 0.47547,0,9.9,"0",0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21 322 | 0.1676,0,7.38,"0",0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8 323 | 0.18159,0,7.38,"0",0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1 324 | 0.35114,0,7.38,"0",0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4 325 | 0.28392,0,7.38,"0",0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5 326 | 0.34109,0,7.38,"0",0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25 327 | 0.19186,0,7.38,"0",0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6 328 | 0.30347,0,7.38,"0",0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23 329 | 0.24103,0,7.38,"0",0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2 330 | 0.06617,0,3.24,"0",0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3 331 | 0.06724,0,3.24,"0",0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6 332 | 0.04544,0,3.24,"0",0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8 333 | 0.05023,35,6.06,"0",0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1 334 | 0.03466,35,6.06,"0",0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4 335 | 0.05083,0,5.19,"0",0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2 336 | 0.03738,0,5.19,"0",0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7 337 | 0.03961,0,5.19,"0",0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1 338 | 0.03427,0,5.19,"0",0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5 339 | 0.03041,0,5.19,"0",0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5 340 | 0.03306,0,5.19,"0",0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6 341 | 0.05497,0,5.19,"0",0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19 342 | 0.06151,0,5.19,"0",0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7 343 | 0.01301,35,1.52,"0",0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7 344 | 0.02498,0,1.89,"0",0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5 345 | 0.02543,55,3.78,"0",0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9 346 | 0.03049,55,3.78,"0",0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2 347 | 0.03113,0,4.39,"0",0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5 348 | 0.06162,0,4.39,"0",0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2 349 | 0.0187,85,4.15,"0",0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1 350 | 0.01501,80,2.01,"0",0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5 351 | 0.02899,40,1.25,"0",0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6 352 | 0.06211,40,1.25,"0",0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9 353 | 0.0795,60,1.69,"0",0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1 354 | 0.07244,60,1.69,"0",0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6 355 | 0.01709,90,2.02,"0",0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1 356 | 0.04301,80,1.91,"0",0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2 357 | 0.10659,80,1.91,"0",0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6 358 | 8.98296,0,18.1,"1",0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8 359 | 3.8497,0,18.1,"1",0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7 360 | 5.20177,0,18.1,"1",0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7 361 | 4.26131,0,18.1,"0",0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6 362 | 4.54192,0,18.1,"0",0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25 363 | 3.83684,0,18.1,"0",0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9 364 | 3.67822,0,18.1,"0",0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8 365 | 4.22239,0,18.1,"1",0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8 366 | 3.47428,0,18.1,"1",0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9 367 | 4.55587,0,18.1,"0",0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5 368 | 3.69695,0,18.1,"0",0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9 369 | 13.5222,0,18.1,"0",0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1 370 | 4.89822,0,18.1,"0",0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50 371 | 5.66998,0,18.1,"1",0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50 372 | 6.53876,0,18.1,"1",0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50 373 | 9.2323,0,18.1,"0",0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50 374 | 8.26725,0,18.1,"1",0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50 375 | 11.1081,0,18.1,"0",0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8 376 | 18.4982,0,18.1,"0",0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8 377 | 19.6091,0,18.1,"0",0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15 378 | 15.288,0,18.1,"0",0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9 379 | 9.82349,0,18.1,"0",0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3 380 | 23.6482,0,18.1,"0",0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1 381 | 17.8667,0,18.1,"0",0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2 382 | 88.9762,0,18.1,"0",0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4 383 | 15.8744,0,18.1,"0",0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9 384 | 9.18702,0,18.1,"0",0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3 385 | 7.99248,0,18.1,"0",0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3 386 | 20.0849,0,18.1,"0",0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8 387 | 16.8118,0,18.1,"0",0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2 388 | 24.3938,0,18.1,"0",0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5 389 | 22.5971,0,18.1,"0",0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4 390 | 14.3337,0,18.1,"0",0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2 391 | 8.15174,0,18.1,"0",0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5 392 | 6.96215,0,18.1,"0",0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1 393 | 5.29305,0,18.1,"0",0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2 394 | 11.5779,0,18.1,"0",0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7 395 | 8.64476,0,18.1,"0",0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8 396 | 13.3598,0,18.1,"0",0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7 397 | 8.71675,0,18.1,"0",0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1 398 | 5.87205,0,18.1,"0",0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5 399 | 7.67202,0,18.1,"0",0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5 400 | 38.3518,0,18.1,"0",0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5 401 | 9.91655,0,18.1,"0",0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3 402 | 25.0461,0,18.1,"0",0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6 403 | 14.2362,0,18.1,"0",0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2 404 | 9.59571,0,18.1,"0",0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1 405 | 24.8017,0,18.1,"0",0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3 406 | 41.5292,0,18.1,"0",0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5 407 | 67.9208,0,18.1,"0",0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5 408 | 20.7162,0,18.1,"0",0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9 409 | 11.9511,0,18.1,"0",0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9 410 | 7.40389,0,18.1,"0",0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2 411 | 14.4383,0,18.1,"0",0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5 412 | 51.1358,0,18.1,"0",0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15 413 | 14.0507,0,18.1,"0",0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2 414 | 18.811,0,18.1,"0",0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9 415 | 28.6558,0,18.1,"0",0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3 416 | 45.7461,0,18.1,"0",0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7 417 | 18.0846,0,18.1,"0",0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2 418 | 10.8342,0,18.1,"0",0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5 419 | 25.9406,0,18.1,"0",0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4 420 | 73.5341,0,18.1,"0",0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8 421 | 11.8123,0,18.1,"0",0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4 422 | 11.0874,0,18.1,"0",0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7 423 | 7.02259,0,18.1,"0",0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2 424 | 12.0482,0,18.1,"0",0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8 425 | 7.05042,0,18.1,"0",0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4 426 | 8.79212,0,18.1,"0",0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7 427 | 15.8603,0,18.1,"0",0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3 428 | 12.2472,0,18.1,"0",0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2 429 | 37.6619,0,18.1,"0",0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9 430 | 7.36711,0,18.1,"0",0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11 431 | 9.33889,0,18.1,"0",0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5 432 | 8.49213,0,18.1,"0",0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5 433 | 10.0623,0,18.1,"0",0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1 434 | 6.44405,0,18.1,"0",0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1 435 | 5.58107,0,18.1,"0",0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3 436 | 13.9134,0,18.1,"0",0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7 437 | 11.1604,0,18.1,"0",0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4 438 | 14.4208,0,18.1,"0",0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6 439 | 15.1772,0,18.1,"0",0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7 440 | 13.6781,0,18.1,"0",0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4 441 | 9.39063,0,18.1,"0",0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8 442 | 22.0511,0,18.1,"0",0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5 443 | 9.72418,0,18.1,"0",0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1 444 | 5.66637,0,18.1,"0",0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4 445 | 9.96654,0,18.1,"0",0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4 446 | 12.8023,0,18.1,"0",0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8 447 | 10.6718,0,18.1,"0",0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8 448 | 6.28807,0,18.1,"0",0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9 449 | 9.92485,0,18.1,"0",0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6 450 | 9.32909,0,18.1,"0",0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1 451 | 7.52601,0,18.1,"0",0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13 452 | 6.71772,0,18.1,"0",0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4 453 | 5.44114,0,18.1,"0",0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2 454 | 5.09017,0,18.1,"0",0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1 455 | 8.24809,0,18.1,"0",0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8 456 | 9.51363,0,18.1,"0",0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9 457 | 4.75237,0,18.1,"0",0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1 458 | 4.66883,0,18.1,"0",0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7 459 | 8.20058,0,18.1,"0",0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5 460 | 7.75223,0,18.1,"0",0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9 461 | 6.80117,0,18.1,"0",0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20 462 | 4.81213,0,18.1,"0",0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4 463 | 3.69311,0,18.1,"0",0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7 464 | 6.65492,0,18.1,"0",0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5 465 | 5.82115,0,18.1,"0",0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2 466 | 7.83932,0,18.1,"0",0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4 467 | 3.1636,0,18.1,"0",0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9 468 | 3.77498,0,18.1,"0",0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19 469 | 4.42228,0,18.1,"0",0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1 470 | 15.5757,0,18.1,"0",0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1 471 | 13.0751,0,18.1,"0",0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1 472 | 4.34879,0,18.1,"0",0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9 473 | 4.03841,0,18.1,"0",0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6 474 | 3.56868,0,18.1,"0",0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2 475 | 4.64689,0,18.1,"0",0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8 476 | 8.05579,0,18.1,"0",0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8 477 | 6.39312,0,18.1,"0",0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3 478 | 4.87141,0,18.1,"0",0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7 479 | 15.0234,0,18.1,"0",0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12 480 | 10.233,0,18.1,"0",0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6 481 | 14.3337,0,18.1,"0",0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4 482 | 5.82401,0,18.1,"0",0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23 483 | 5.70818,0,18.1,"0",0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7 484 | 5.73116,0,18.1,"0",0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25 485 | 2.81838,0,18.1,"0",0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8 486 | 2.37857,0,18.1,"0",0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6 487 | 3.67367,0,18.1,"0",0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2 488 | 5.69175,0,18.1,"0",0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1 489 | 4.83567,0,18.1,"0",0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6 490 | 0.15086,0,27.74,"0",0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2 491 | 0.18337,0,27.74,"0",0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7 492 | 0.20746,0,27.74,"0",0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1 493 | 0.10574,0,27.74,"0",0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6 494 | 0.11132,0,27.74,"0",0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1 495 | 0.17331,0,9.69,"0",0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8 496 | 0.27957,0,9.69,"0",0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5 497 | 0.17899,0,9.69,"0",0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1 498 | 0.2896,0,9.69,"0",0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7 499 | 0.26838,0,9.69,"0",0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3 500 | 0.23912,0,9.69,"0",0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2 501 | 0.17783,0,9.69,"0",0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5 502 | 0.22438,0,9.69,"0",0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8 503 | 0.06263,0,11.93,"0",0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4 504 | 0.04527,0,11.93,"0",0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6 505 | 0.06076,0,11.93,"0",0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9 506 | 0.10959,0,11.93,"0",0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22 507 | 0.04741,0,11.93,"0",0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9 -------------------------------------------------------------------------------- /models/data/test_petrol_consumption.csv: -------------------------------------------------------------------------------- 1 | Petrol_tax,Average_income,Paved_Highways,Population_Driver_licence(%),Petrol_Consumption 2 | 9.00,4476,3942,0.5710,510 3 | 7.00,5002,9794,0.5930,524 4 | -------------------------------------------------------------------------------- /models/images/databricks_ce_download_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/models/images/databricks_ce_download_notebooks.png -------------------------------------------------------------------------------- /models/images/mlflow-models-python-model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/models/images/mlflow-models-python-model.png -------------------------------------------------------------------------------- /models/images/mlflow-workshop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/models/images/mlflow-workshop.png -------------------------------------------------------------------------------- /models/images/sentiment_analysis.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/models/images/sentiment_analysis.jpg -------------------------------------------------------------------------------- /models/notebooks/dbc/MLflow-Models.dbc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/models/notebooks/dbc/MLflow-Models.dbc -------------------------------------------------------------------------------- /models/slides/mlflow-models-module.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/models/slides/mlflow-models-module.pdf -------------------------------------------------------------------------------- /projects/README.md: -------------------------------------------------------------------------------- 1 | Managing the Complete Machine Learning Lifecycle with MLflow 2 | ============================================================= 3 | ![](./images/mlflow-workshop.png) 4 | 5 | Module 2 of 4 6 | ------------- 7 | Other parts: 8 | - [Module 1](../tracking/README.md) 9 | - [Module 3](../models/README.md) 10 | - [Module 4](../model_registery/README.md) 11 | 12 | Content for the MLflow Workshop Series 13 | --------------------------------------- 14 | Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. 15 | 16 | To solve these challenges, [MLflow](https://mlflow.org), an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, 17 | encapsulate models that can be used with many existing tools, and central respositry to share models, 18 | accelerating the ML lifecycle for organizations of any size. 19 | 20 | Goal and Objective 21 | ------------------ 22 | Aimed at beginner or intermediate level, this four-part modules aims to educate data scientists or ML developer in how you 23 | leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing. 24 | 25 | What you will learn 26 | ------------------- 27 | Understand the four main components of open source MLflow——MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each compopnent helps address challenges of the ML lifecycle. 28 | * How to use [MLflow Tracking](https://mlflow.org/docs/latest/tracking.html) to record and query experiments: code, data, config, and results. 29 | * How to use [MLflow Projects](https://mlflow.org/docs/latest/projects.html) packaging format to reproduce runs 30 | * How to use [MLflow Models](https://mlflow.org/docs/latest/models.html) general format to send models to diverse deployment tools. 31 | * How to use [Model Registry](https://mlflow.org/docs/latest/model-registry.html) for collaborative model lifecycle management 32 | * How to use [MLflow UI](https://mlflow.org/docs/latest/tracking.html#tracking-ui) to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics 33 | 34 | 35 | Instructor 36 | ----------- 37 | 38 | - [Jules S. Damji](https://www.linkedin.com/in/dmatrix/) [@2twitme](https://twitter.com/2twitme) 39 | --- 40 | 41 | 42 | About the MLflow Project module 2 43 | ---------------------------------- 44 | 45 | In this module 2, we will cover: 46 | * Concepts and motivation behind MLflow Projects 47 | * Tour of the MLflow Project API Documentation 48 | * How to execute and reproduce MLflow Projects in the Databricks Community Edition (DCE) 49 | * Build an MLflow Project and share it for reproducible runs 50 | * Use the MLflow UI on the DCE 51 | * Use the MLflow UI as part of DCE to compare experiment metrics, parameters, and runs 52 | 53 | Prerequisites 54 | ------------- 55 | * Before the session, please pre-register for [Databricks Community Edition](https://databricks.com/try-databricks) 56 | * Knowledge of Python 3 and programming in general 57 | * Preferably a UNIX-based, fully-charged laptop with 8-16 GB, with a Chrome or Firefox browser 58 | * Familiarity with GitHub, git, and an account on Github 59 | * Some knowledge of Machine Learning concepts, libraries, and frameworks 60 | * scikit-learn 61 | * pandas and Numpy 62 | * matplotlib 63 | * Keras/TensorFlow 64 | * [**optional for module-1**] PyCharm/IntelliJ or choice of syntax-based Python editor 65 | * [**optional for module-1**] pip/pip3 or conda and Python 3 installed 66 | * Loads of virtual laughter, curiosity, and a sense of humor ... :-) 67 | 68 | Obtaining the Tutorial Material 69 | -------------------------------- 70 | 71 | Familiarity with **git** is important so that you can get all the material easily during the tutorial and 72 | workshop as well as continue to work in your free time, after the session is over. 73 | 74 | ``` git clone github.com:dmatrix/tmls-workshop.git or git clone https://github.com/dmatrix/tmls-workshop.git``` 75 | 76 | 77 | Documentation Resources 78 | ----------------------- 79 | 80 | This tutorial will refer to documentation: 81 | 82 | 1. [MLflow](https://mlflow.org/docs/latest/index.html) 83 | 2. [Numpy](https://numpy.org/devdocs/user/quickstart.html) 84 | 3. [Pandas](https://pandas.pydata.org/pandas-docs/stable/reference/index.html) 85 | 4. [Scikit-Learn](https://scikit-learn.org/stable/index.html) 86 | 5. [Keras](https://keras.io/optimizers/) 87 | 6. [TensorFlow](https://tensorflow.org) 88 | 7. [Matplotlib](https://matplotlib.org/3.2.0/tutorials/introductory/pyplot.html) 89 | 90 | How to get started 91 | ------------------- 92 | We will walk through this during the session, but please sign up for [Databricks Community Edition](https://databricks.com/try-databricks) before the session : 93 | 94 | 1. ``` git clone github.com:dmatrix/tmls-workshop.git or git clone https://github.com/dmatrix/tmls-workshop.git``` 95 | 2. Use this [URL](https://community.cloud.databricks.com/login.html) to log into the Databricks Community Edition 96 | 97 | ![](../images/databricks_ce_loging.png) 98 | 99 | 3. Create a **8.2 ML (includes Apache Spark 3.1.1, Scala 2.12)** cluster 100 | 101 | ![](../images/databricks_ce_create_mlr.png) 102 | 103 | 4. In the browser: 104 | * (1) Go the GitHub **projects/notebooks/dbc/** subdirectory 105 | * (2) Download **MLflow-Projects.dbc** file on your laptop 106 | 107 | 5. Import the **MLflow-Projects.dbc** file into the Databricks Community Edition 108 | 109 | ![](../images/databricks_ce_import_notebooks.png) 110 | 111 | Let's go! 112 | 113 | Cheers, 114 | 115 | Jules 116 | -------------------------------------------------------------------------------- /projects/images/databricks_ce_download_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/projects/images/databricks_ce_download_notebooks.png -------------------------------------------------------------------------------- /projects/images/mlflow-workshop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/projects/images/mlflow-workshop.png -------------------------------------------------------------------------------- /projects/notebooks/dbc/MLflow-Projects.dbc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/projects/notebooks/dbc/MLflow-Projects.dbc -------------------------------------------------------------------------------- /projects/slides/mlflow-projects-module.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/projects/slides/mlflow-projects-module.pdf -------------------------------------------------------------------------------- /tracking/README.md: -------------------------------------------------------------------------------- 1 | Managing the Complete Machine Learning Lifecycle with MLflow 2 | ============================================================= 3 | ![](./images/mlflow-workshop.png) 4 | 5 | Module 1 of 4 6 | ------------- 7 | Other parts: 8 | - [Module 2](../projects/README.md) 9 | - [Module 3](../models/README.md) 10 | - [Module 4](../model_registery/README.md) 11 | 12 | Content for the MLflow Workshop Series 13 | --------------------------------------- 14 | Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. 15 | 16 | To solve these challenges, [MLflow](https://mlflow.org), an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, 17 | encapsulate models that can be used with many existing tools, and central respositry to share models, 18 | accelerating the ML lifecycle for organizations of any size. 19 | 20 | Goal and Objective 21 | ------------------ 22 | Aimed at beginner or intermediate level, this four-part modules aims to educate data scientists or ML developer in how you 23 | leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing. 24 | 25 | What you will learn 26 | ------------------- 27 | Understand the four main components of open source MLflow——MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each compopnent helps address challenges of the ML lifecycle. 28 | * How to use [MLflow Tracking](https://mlflow.org/docs/latest/tracking.html) to record and query experiments: code, data, config, and results. 29 | * How to use [MLflow Projects](https://mlflow.org/docs/latest/projects.html) packaging format to reproduce runs 30 | * How to use [MLflow Models](https://mlflow.org/docs/latest/models.html) general format to send models to diverse deployment tools. 31 | * How to use [Model Registry](https://mlflow.org/docs/latest/model-registry.html) for collaborative model lifecycle management 32 | * How to use [MLflow UI](https://mlflow.org/docs/latest/tracking.html#tracking-ui) to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics 33 | 34 | 35 | Instructor 36 | ----------- 37 | 38 | - [Jules S. Damji](https://www.linkedin.com/in/dmatrix/) [@2twitme](https://twitter.com/2twitme) 39 | --- 40 | 41 | 42 | About the MLflow tracking module 1 43 | ---------------------------------- 44 | 45 | In this module 1, we will cover: 46 | * What and why MLflow and how MLflow addresses ML lifecycle 47 | * Tour through the MLflow APIs docs 48 | * Learn how to use Databricks Community Edition (DCE) 49 | * Introduce MLflow Python Fluent Tracking APIs 50 | * Walk and work through a machine learning model using MLflow APIs in the DCE 51 | * Use the MLflow UI as part of DCE to compare experiment metrics, parameters, and runs 52 | 53 | Prerequisites 54 | ------------- 55 | * Before the session, please pre-register for [Databricks Community Edition](https://databricks.com/try-databricks) 56 | * Knowledge of Python 3 and programming in general 57 | * Preferably a UNIX-based, fully-charged laptop with 8-16 GB, with a Chrome or Firefox browser 58 | * Familiarity with GitHub, git, and an account on Github 59 | * Some knowledge of Machine Learning concepts, libraries, and frameworks 60 | * scikit-learn 61 | * pandas and Numpy 62 | * matplotlib 63 | * [**optional for module-1**] PyCharm/IntelliJ or choice of syntax-based Python editor 64 | * [**optional for module-1**] pip/pip3 or conda and Python 3 installed 65 | * Loads of virtual laughter, curiosity, and a sense of humor ... :-) 66 | 67 | Obtaining the Tutorial Material 68 | -------------------------------- 69 | 70 | Familiarity with **git** is important so that you can get all the material easily during the tutorial and 71 | workshop as well as continue to work in your free time, after the session is over. 72 | 73 | ``` git clone github.com:dmatrix/tmls-workshop.git or git clone https://github.com/dmatrix/tmls-workshop.git``` 74 | 75 | Documentation Resources 76 | ----------------------- 77 | 78 | This tutorial will refer to documentation: 79 | 80 | 1. [MLflow](https://mlflow.org/docs/latest/index.html) 81 | 2. [Numpy](https://numpy.org/devdocs/user/quickstart.html) 82 | 3. [Pandas](https://pandas.pydata.org/pandas-docs/stable/reference/index.html) 83 | 4. [Scikit-Learn](https://scikit-learn.org/stable/index.html) 84 | 5. [Keras](https://keras.io/optimizers/) 85 | 6. [TensorFlow](https://tensorflow.org) 86 | 7. [Matplotlib](https://matplotlib.org/3.2.0/tutorials/introductory/pyplot.html) 87 | 88 | How to get started 89 | ------------------- 90 | We will walk through this during the session, but please sign up for [Databricks Community Edition](https://databricks.com/try-databricks) before the session : 91 | 92 | 1. ``` git clone github.com:dmatrix/tmls-workshop.git or git clone https://github.com/dmatrix/tmls-workshop.git``` 93 | 2. Use this [URL](https://community.cloud.databricks.com/login.html) to log into the Databricks Community Edition 94 | 95 | ![](../images/databricks_ce_loging.png) 96 | 97 | 3. Create a **8.2 ML (includes Apache Spark 3.1.1, Scala 2.12)** cluster 98 | 99 | ![](../images/databricks_ce_create_mlr.png) 100 | 101 | 4. In the browser: 102 | * (1) Go the GitHub **tracking/notebooks/dbc/** subdirectory 103 | * (2) Download **MLflow-Tracking.dbc** file on your laptop 104 | 105 | 5. Import the **MLflow-Tracking.dbc** file into the Databricks Community Edition 106 | 107 | ![](images/databricks_ce_import_notebooks.png) 108 | 109 | Let's go! 110 | 111 | Cheers, 112 | 113 | Jules 114 | -------------------------------------------------------------------------------- /tracking/data/bill_authentication.csv: -------------------------------------------------------------------------------- 1 | Variance,Skewness,Curtosis,Entropy,Class 2 | 3.6216,8.6661,-2.8073,-0.44699,0 3 | 4.5459,8.1674,-2.4586,-1.4621,0 4 | 3.866,-2.6383,1.9242,0.10645,0 5 | 3.4566,9.5228,-4.0112,-3.5944,0 6 | 0.32924,-4.4552,4.5718,-0.9888,0 7 | 4.3684,9.6718,-3.9606,-3.1625,0 8 | 3.5912,3.0129,0.72888,0.56421,0 9 | 2.0922,-6.81,8.4636,-0.60216,0 10 | 3.2032,5.7588,-0.75345,-0.61251,0 11 | 1.5356,9.1772,-2.2718,-0.73535,0 12 | 1.2247,8.7779,-2.2135,-0.80647,0 13 | 3.9899,-2.7066,2.3946,0.86291,0 14 | 1.8993,7.6625,0.15394,-3.1108,0 15 | -1.5768,10.843,2.5462,-2.9362,0 16 | 3.404,8.7261,-2.9915,-0.57242,0 17 | 4.6765,-3.3895,3.4896,1.4771,0 18 | 2.6719,3.0646,0.37158,0.58619,0 19 | 0.80355,2.8473,4.3439,0.6017,0 20 | 1.4479,-4.8794,8.3428,-2.1086,0 21 | 5.2423,11.0272,-4.353,-4.1013,0 22 | 5.7867,7.8902,-2.6196,-0.48708,0 23 | 0.3292,-4.4552,4.5718,-0.9888,0 24 | 3.9362,10.1622,-3.8235,-4.0172,0 25 | 0.93584,8.8855,-1.6831,-1.6599,0 26 | 4.4338,9.887,-4.6795,-3.7483,0 27 | 0.7057,-5.4981,8.3368,-2.8715,0 28 | 1.1432,-3.7413,5.5777,-0.63578,0 29 | -0.38214,8.3909,2.1624,-3.7405,0 30 | 6.5633,9.8187,-4.4113,-3.2258,0 31 | 4.8906,-3.3584,3.4202,1.0905,0 32 | -0.24811,-0.17797,4.9068,0.15429,0 33 | 1.4884,3.6274,3.308,0.48921,0 34 | 4.2969,7.617,-2.3874,-0.96164,0 35 | -0.96511,9.4111,1.7305,-4.8629,0 36 | -1.6162,0.80908,8.1628,0.60817,0 37 | 2.4391,6.4417,-0.80743,-0.69139,0 38 | 2.6881,6.0195,-0.46641,-0.69268,0 39 | 3.6289,0.81322,1.6277,0.77627,0 40 | 4.5679,3.1929,-2.1055,0.29653,0 41 | 3.4805,9.7008,-3.7541,-3.4379,0 42 | 4.1711,8.722,-3.0224,-0.59699,0 43 | -0.2062,9.2207,-3.7044,-6.8103,0 44 | -0.0068919,9.2931,-0.41243,-1.9638,0 45 | 0.96441,5.8395,2.3235,0.066365,0 46 | 2.8561,6.9176,-0.79372,0.48403,0 47 | -0.7869,9.5663,-3.7867,-7.5034,0 48 | 2.0843,6.6258,0.48382,-2.2134,0 49 | -0.7869,9.5663,-3.7867,-7.5034,0 50 | 3.9102,6.065,-2.4534,-0.68234,0 51 | 1.6349,3.286,2.8753,0.087054,0 52 | 4.3239,-4.8835,3.4356,-0.5776,0 53 | 5.262,3.9834,-1.5572,1.0103,0 54 | 3.1452,5.825,-0.51439,-1.4944,0 55 | 2.549,6.1499,-1.1605,-1.2371,0 56 | 4.9264,5.496,-2.4774,-0.50648,0 57 | 4.8265,0.80287,1.6371,1.1875,0 58 | 2.5635,6.7769,-0.61979,0.38576,0 59 | 5.807,5.0097,-2.2384,0.43878,0 60 | 3.1377,-4.1096,4.5701,0.98963,0 61 | -0.78289,11.3603,-0.37644,-7.0495,0 62 | 2.888,0.44696,4.5907,-0.24398,0 63 | 0.49665,5.527,1.7785,-0.47156,0 64 | 4.2586,11.2962,-4.0943,-4.3457,0 65 | 1.7939,-1.1174,1.5454,-0.26079,0 66 | 5.4021,3.1039,-1.1536,1.5651,0 67 | 2.5367,2.599,2.0938,0.20085,0 68 | 4.6054,-4.0765,2.7587,0.31981,0 69 | 2.4235,9.5332,-3.0789,-2.7746,0 70 | 1.0009,7.7846,-0.28219,-2.6608,0 71 | 0.12326,8.9848,-0.9351,-2.4332,0 72 | 3.9529,-2.3548,2.3792,0.48274,0 73 | 4.1373,0.49248,1.093,1.8276,0 74 | 4.7181,10.0153,-3.9486,-3.8582,0 75 | 4.1654,-3.4495,3.643,1.0879,0 76 | 4.4069,10.9072,-4.5775,-4.4271,0 77 | 2.3066,3.5364,0.57551,0.41938,0 78 | 3.7935,7.9853,-2.5477,-1.872,0 79 | 0.049175,6.1437,1.7828,-0.72113,0 80 | 0.24835,7.6439,0.9885,-0.87371,0 81 | 1.1317,3.9647,3.3979,0.84351,0 82 | 2.8033,9.0862,-3.3668,-1.0224,0 83 | 4.4682,2.2907,0.95766,0.83058,0 84 | 5.0185,8.5978,-2.9375,-1.281,0 85 | 1.8664,7.7763,-0.23849,-2.9634,0 86 | 3.245,6.63,-0.63435,0.86937,0 87 | 4.0296,2.6756,0.80685,0.71679,0 88 | -1.1313,1.9037,7.5339,1.022,0 89 | 0.87603,6.8141,0.84198,-0.17156,0 90 | 4.1197,-2.7956,2.0707,0.67412,0 91 | 3.8027,0.81529,2.1041,1.0245,0 92 | 1.4806,7.6377,-2.7876,-1.0341,0 93 | 4.0632,3.584,0.72545,0.39481,0 94 | 4.3064,8.2068,-2.7824,-1.4336,0 95 | 2.4486,-6.3175,7.9632,0.20602,0 96 | 3.2718,1.7837,2.1161,0.61334,0 97 | -0.64472,-4.6062,8.347,-2.7099,0 98 | 2.9543,1.076,0.64577,0.89394,0 99 | 2.1616,-6.8804,8.1517,-0.081048,0 100 | 3.82,10.9279,-4.0112,-5.0284,0 101 | -2.7419,11.4038,2.5394,-5.5793,0 102 | 3.3669,-5.1856,3.6935,-1.1427,0 103 | 4.5597,-2.4211,2.6413,1.6168,0 104 | 5.1129,-0.49871,0.62863,1.1189,0 105 | 3.3397,-4.6145,3.9823,-0.23751,0 106 | 4.2027,0.22761,0.96108,0.97282,0 107 | 3.5438,1.2395,1.997,2.1547,0 108 | 2.3136,10.6651,-3.5288,-4.7672,0 109 | -1.8584,7.886,-1.6643,-1.8384,0 110 | 3.106,9.5414,-4.2536,-4.003,0 111 | 2.9163,10.8306,-3.3437,-4.122,0 112 | 3.9922,-4.4676,3.7304,-0.1095,0 113 | 1.518,5.6946,0.094818,-0.026738,0 114 | 3.2351,9.647,-3.2074,-2.5948,0 115 | 4.2188,6.8162,-1.2804,0.76076,0 116 | 1.7819,6.9176,-1.2744,-1.5759,0 117 | 2.5331,2.9135,-0.822,-0.12243,0 118 | 3.8969,7.4163,-1.8245,0.14007,0 119 | 2.108,6.7955,-0.1708,0.4905,0 120 | 2.8969,0.70768,2.29,1.8663,0 121 | 0.9297,-3.7971,4.6429,-0.2957,0 122 | 3.4642,10.6878,-3.4071,-4.109,0 123 | 4.0713,10.4023,-4.1722,-4.7582,0 124 | -1.4572,9.1214,1.7425,-5.1241,0 125 | -1.5075,1.9224,7.1466,0.89136,0 126 | -0.91718,9.9884,1.1804,-5.2263,0 127 | 2.994,7.2011,-1.2153,0.3211,0 128 | -2.343,12.9516,3.3285,-5.9426,0 129 | 3.7818,-2.8846,2.2558,-0.15734,0 130 | 4.6689,1.3098,0.055404,1.909,0 131 | 3.4663,1.1112,1.7425,1.3388,0 132 | 3.2697,-4.3414,3.6884,-0.29829,0 133 | 5.1302,8.6703,-2.8913,-1.5086,0 134 | 2.0139,6.1416,0.37929,0.56938,0 135 | 0.4339,5.5395,2.033,-0.40432,0 136 | -1.0401,9.3987,0.85998,-5.3336,0 137 | 4.1605,11.2196,-3.6136,-4.0819,0 138 | 5.438,9.4669,-4.9417,-3.9202,0 139 | 5.032,8.2026,-2.6256,-1.0341,0 140 | 5.2418,10.5388,-4.1174,-4.2797,0 141 | -0.2062,9.2207,-3.7044,-6.8103,0 142 | 2.0911,0.94358,4.5512,1.234,0 143 | 1.7317,-0.34765,4.1905,-0.99138,0 144 | 4.1736,3.3336,-1.4244,0.60429,0 145 | 3.9232,-3.2467,3.4579,0.83705,0 146 | 3.8481,10.1539,-3.8561,-4.2228,0 147 | 0.5195,-3.2633,3.0895,-0.9849,0 148 | 3.8584,0.78425,1.1033,1.7008,0 149 | 1.7496,-0.1759,5.1827,1.2922,0 150 | 3.6277,0.9829,0.68861,0.63403,0 151 | 2.7391,7.4018,0.071684,-2.5302,0 152 | 4.5447,8.2274,-2.4166,-1.5875,0 153 | -1.7599,11.9211,2.6756,-3.3241,0 154 | 5.0691,0.21313,0.20278,1.2095,0 155 | 3.4591,11.112,-4.2039,-5.0931,0 156 | 1.9358,8.1654,-0.023425,-2.2586,0 157 | 2.486,-0.99533,5.3404,-0.15475,0 158 | 2.4226,-4.5752,5.947,0.21507,0 159 | 3.9479,-3.7723,2.883,0.019813,0 160 | 2.2634,-4.4862,3.6558,-0.61251,0 161 | 1.3566,4.2358,2.1341,0.3211,0 162 | 5.0452,3.8964,-1.4304,0.86291,0 163 | 3.5499,8.6165,-3.2794,-1.2009,0 164 | 0.17346,7.8695,0.26876,-3.7883,0 165 | 2.4008,9.3593,-3.3565,-3.3526,0 166 | 4.8851,1.5995,-0.00029081,1.6401,0 167 | 4.1927,-3.2674,2.5839,0.21766,0 168 | 1.1166,8.6496,-0.96252,-1.8112,0 169 | 1.0235,6.901,-2.0062,-2.7125,0 170 | -1.803,11.8818,2.0458,-5.2728,0 171 | 0.11739,6.2761,-1.5495,-2.4746,0 172 | 0.5706,-0.0248,1.2421,-0.5621,0 173 | 4.0552,-2.4583,2.2806,1.0323,0 174 | -1.6952,1.0657,8.8294,0.94955,0 175 | -1.1193,10.7271,2.0938,-5.6504,0 176 | 1.8799,2.4707,2.4931,0.37671,0 177 | 3.583,-3.7971,3.4391,-0.12501,0 178 | 0.19081,9.1297,-3.725,-5.8224,0 179 | 3.6582,5.6864,-1.7157,-0.23751,0 180 | -0.13144,-1.7775,8.3316,0.35214,0 181 | 2.3925,9.798,-3.0361,-2.8224,0 182 | 1.6426,3.0149,0.22849,-0.147,0 183 | -0.11783,-1.5789,8.03,-0.028031,0 184 | -0.69572,8.6165,1.8419,-4.3289,0 185 | 2.9421,7.4101,-0.97709,-0.88406,0 186 | -1.7559,11.9459,3.0946,-4.8978,0 187 | -1.2537,10.8803,1.931,-4.3237,0 188 | 3.2585,-4.4614,3.8024,-0.15087,0 189 | 1.8314,6.3672,-0.036278,0.049554,0 190 | 4.5645,-3.6275,2.8684,0.27714,0 191 | 2.7365,-5.0325,6.6608,-0.57889,0 192 | 0.9297,-3.7971,4.6429,-0.2957,0 193 | 3.9663,10.1684,-4.1131,-4.6056,0 194 | 1.4578,-0.08485,4.1785,0.59136,0 195 | 4.8272,3.0687,0.68604,0.80731,0 196 | -2.341,12.3784,0.70403,-7.5836,0 197 | -1.8584,7.886,-1.6643,-1.8384,0 198 | 4.1454,7.257,-1.9153,-0.86078,0 199 | 1.9157,6.0816,0.23705,-2.0116,0 200 | 4.0215,-2.1914,2.4648,1.1409,0 201 | 5.8862,5.8747,-2.8167,-0.30087,0 202 | -2.0897,10.8265,2.3603,-3.4198,0 203 | 4.0026,-3.5943,3.5573,0.26809,0 204 | -0.78689,9.5663,-3.7867,-7.5034,0 205 | 4.1757,10.2615,-3.8552,-4.3056,0 206 | 0.83292,7.5404,0.65005,-0.92544,0 207 | 4.8077,2.2327,-0.26334,1.5534,0 208 | 5.3063,5.2684,-2.8904,-0.52716,0 209 | 2.5605,9.2683,-3.5913,-1.356,0 210 | 2.1059,7.6046,-0.47755,-1.8461,0 211 | 2.1721,-0.73874,5.4672,-0.72371,0 212 | 4.2899,9.1814,-4.6067,-4.3263,0 213 | 3.5156,10.1891,-4.2759,-4.978,0 214 | 2.614,8.0081,-3.7258,-1.3069,0 215 | 0.68087,2.3259,4.9085,0.54998,0 216 | 4.1962,0.74493,0.83256,0.753,0 217 | 6.0919,2.9673,-1.3267,1.4551,0 218 | 1.3234,3.2964,0.2362,-0.11984,0 219 | 1.3264,1.0326,5.6566,-0.41337,0 220 | -0.16735,7.6274,1.2061,-3.6241,0 221 | -1.3,10.2678,-2.953,-5.8638,0 222 | -2.2261,12.5398,2.9438,-3.5258,0 223 | 2.4196,6.4665,-0.75688,0.228,0 224 | 1.0987,0.6394,5.989,-0.58277,0 225 | 4.6464,10.5326,-4.5852,-4.206,0 226 | -0.36038,4.1158,3.1143,-0.37199,0 227 | 1.3562,3.2136,4.3465,0.78662,0 228 | 0.5706,-0.0248,1.2421,-0.5621,0 229 | -2.6479,10.1374,-1.331,-5.4707,0 230 | 3.1219,-3.137,1.9259,-0.37458,0 231 | 5.4944,1.5478,0.041694,1.9284,0 232 | -1.3389,1.552,7.0806,1.031,0 233 | -2.3361,11.9604,3.0835,-5.4435,0 234 | 2.2596,-0.033118,4.7355,-0.2776,0 235 | 0.46901,-0.63321,7.3848,0.36507,0 236 | 2.7296,2.8701,0.51124,0.5099,0 237 | 2.0466,2.03,2.1761,-0.083634,0 238 | -1.3274,9.498,2.4408,-5.2689,0 239 | 3.8905,-2.1521,2.6302,1.1047,0 240 | 3.9994,0.90427,1.1693,1.6892,0 241 | 2.3952,9.5083,-3.1783,-3.0086,0 242 | 3.2704,6.9321,-1.0456,0.23447,0 243 | -1.3931,1.5664,7.5382,0.78403,0 244 | 1.6406,3.5488,1.3964,-0.36424,0 245 | 2.7744,6.8576,-1.0671,0.075416,0 246 | 2.4287,9.3821,-3.2477,-1.4543,0 247 | 4.2134,-2.806,2.0116,0.67412,0 248 | 1.6472,0.48213,4.7449,1.225,0 249 | 2.0597,-0.99326,5.2119,-0.29312,0 250 | 0.3798,0.7098,0.7572,-0.4444,0 251 | 1.0135,8.4551,-1.672,-2.0815,0 252 | 4.5691,-4.4552,3.1769,0.0042961,0 253 | 0.57461,10.1105,-1.6917,-4.3922,0 254 | 0.5734,9.1938,-0.9094,-1.872,0 255 | 5.2868,3.257,-1.3721,1.1668,0 256 | 4.0102,10.6568,-4.1388,-5.0646,0 257 | 4.1425,-3.6792,3.8281,1.6297,0 258 | 3.0934,-2.9177,2.2232,0.22283,0 259 | 2.2034,5.9947,0.53009,0.84998,0 260 | 3.744,0.79459,0.95851,1.0077,0 261 | 3.0329,2.2948,2.1135,0.35084,0 262 | 3.7731,7.2073,-1.6814,-0.94742,0 263 | 3.1557,2.8908,0.59693,0.79825,0 264 | 1.8114,7.6067,-0.9788,-2.4668,0 265 | 4.988,7.2052,-3.2846,-1.1608,0 266 | 2.483,6.6155,-0.79287,-0.90863,0 267 | 1.594,4.7055,1.3758,0.081882,0 268 | -0.016103,9.7484,0.15394,-1.6134,0 269 | 3.8496,9.7939,-4.1508,-4.4582,0 270 | 0.9297,-3.7971,4.6429,-0.2957,0 271 | 4.9342,2.4107,-0.17594,1.6245,0 272 | 3.8417,10.0215,-4.2699,-4.9159,0 273 | 5.3915,9.9946,-3.8081,-3.3642,0 274 | 4.4072,-0.070365,2.0416,1.1319,0 275 | 2.6946,6.7976,-0.40301,0.44912,0 276 | 5.2756,0.13863,0.12138,1.1435,0 277 | 3.4312,6.2637,-1.9513,-0.36165,0 278 | 4.052,-0.16555,0.45383,0.51248,0 279 | 1.3638,-4.7759,8.4182,-1.8836,0 280 | 0.89566,7.7763,-2.7473,-1.9353,0 281 | 1.9265,7.7557,-0.16823,-3.0771,0 282 | 0.20977,-0.46146,7.7267,0.90946,0 283 | 4.068,-2.9363,2.1992,0.50084,0 284 | 2.877,-4.0599,3.6259,-0.32544,0 285 | 0.3223,-0.89808,8.0883,0.69222,0 286 | -1.3,10.2678,-2.953,-5.8638,0 287 | 1.7747,-6.4334,8.15,-0.89828,0 288 | 1.3419,-4.4221,8.09,-1.7349,0 289 | 0.89606,10.5471,-1.4175,-4.0327,0 290 | 0.44125,2.9487,4.3225,0.7155,0 291 | 3.2422,6.2265,0.12224,-1.4466,0 292 | 2.5678,3.5136,0.61406,-0.40691,0 293 | -2.2153,11.9625,0.078538,-7.7853,0 294 | 4.1349,6.1189,-2.4294,-0.19613,0 295 | 1.934,-9.2828e-06,4.816,-0.33967,0 296 | 2.5068,1.1588,3.9249,0.12585,0 297 | 2.1464,6.0795,-0.5778,-2.2302,0 298 | 0.051979,7.0521,-2.0541,-3.1508,0 299 | 1.2706,8.035,-0.19651,-2.1888,0 300 | 1.143,0.83391,5.4552,-0.56984,0 301 | 2.2928,9.0386,-3.2417,-1.2991,0 302 | 0.3292,-4.4552,4.5718,-0.9888,0 303 | 2.9719,6.8369,-0.2702,0.71291,0 304 | 1.6849,8.7489,-1.2641,-1.3858,0 305 | -1.9177,11.6894,2.5454,-3.2763,0 306 | 2.3729,10.4726,-3.0087,-3.2013,0 307 | 1.0284,9.767,-1.3687,-1.7853,0 308 | 0.27451,9.2186,-3.2863,-4.8448,0 309 | 1.6032,-4.7863,8.5193,-2.1203,0 310 | 4.616,10.1788,-4.2185,-4.4245,0 311 | 4.2478,7.6956,-2.7696,-1.0767,0 312 | 4.0215,-2.7004,2.4957,0.36636,0 313 | 5.0297,-4.9704,3.5025,-0.23751,0 314 | 1.5902,2.2948,3.2403,0.18404,0 315 | 2.1274,5.1939,-1.7971,-1.1763,0 316 | 1.1811,8.3847,-2.0567,-0.90345,0 317 | 0.3292,-4.4552,4.5718,-0.9888,0 318 | 5.7353,5.2808,-2.2598,0.075416,0 319 | 2.6718,5.6574,0.72974,-1.4892,0 320 | 1.5799,-4.7076,7.9186,-1.5487,0 321 | 2.9499,2.2493,1.3458,-0.037083,0 322 | 0.5195,-3.2633,3.0895,-0.9849,0 323 | 3.7352,9.5911,-3.9032,-3.3487,0 324 | -1.7344,2.0175,7.7618,0.93532,0 325 | 3.884,10.0277,-3.9298,-4.0819,0 326 | 3.5257,1.2829,1.9276,1.7991,0 327 | 4.4549,2.4976,1.0313,0.96894,0 328 | -0.16108,-6.4624,8.3573,-1.5216,0 329 | 4.2164,9.4607,-4.9288,-5.2366,0 330 | 3.5152,6.8224,-0.67377,-0.46898,0 331 | 1.6988,2.9094,2.9044,0.11033,0 332 | 1.0607,2.4542,2.5188,-0.17027,0 333 | 2.0421,1.2436,4.2171,0.90429,0 334 | 3.5594,1.3078,1.291,1.6556,0 335 | 3.0009,5.8126,-2.2306,-0.66553,0 336 | 3.9294,1.4112,1.8076,0.89782,0 337 | 3.4667,-4.0724,4.2882,1.5418,0 338 | 3.966,3.9213,0.70574,0.33662,0 339 | 1.0191,2.33,4.9334,0.82929,0 340 | 0.96414,5.616,2.2138,-0.12501,0 341 | 1.8205,6.7562,0.0099913,0.39481,0 342 | 4.9923,7.8653,-2.3515,-0.71984,0 343 | -1.1804,11.5093,0.15565,-6.8194,0 344 | 4.0329,0.23175,0.89082,1.1823,0 345 | 0.66018,10.3878,-1.4029,-3.9151,0 346 | 3.5982,7.1307,-1.3035,0.21248,0 347 | -1.8584,7.886,-1.6643,-1.8384,0 348 | 4.0972,0.46972,1.6671,0.91593,0 349 | 3.3299,0.91254,1.5806,0.39352,0 350 | 3.1088,3.1122,0.80857,0.4336,0 351 | -4.2859,8.5234,3.1392,-0.91639,0 352 | -1.2528,10.2036,2.1787,-5.6038,0 353 | 0.5195,-3.2633,3.0895,-0.9849,0 354 | 0.3292,-4.4552,4.5718,-0.9888,0 355 | 0.88872,5.3449,2.045,-0.19355,0 356 | 3.5458,9.3718,-4.0351,-3.9564,0 357 | -0.21661,8.0329,1.8848,-3.8853,0 358 | 2.7206,9.0821,-3.3111,-0.96811,0 359 | 3.2051,8.6889,-2.9033,-0.7819,0 360 | 2.6917,10.8161,-3.3,-4.2888,0 361 | -2.3242,11.5176,1.8231,-5.375,0 362 | 2.7161,-4.2006,4.1914,0.16981,0 363 | 3.3848,3.2674,0.90967,0.25128,0 364 | 1.7452,4.8028,2.0878,0.62627,0 365 | 2.805,0.57732,1.3424,1.2133,0 366 | 5.7823,5.5788,-2.4089,-0.056479,0 367 | 3.8999,1.734,1.6011,0.96765,0 368 | 3.5189,6.332,-1.7791,-0.020273,0 369 | 3.2294,7.7391,-0.37816,-2.5405,0 370 | 3.4985,3.1639,0.22677,-0.1651,0 371 | 2.1948,1.3781,1.1582,0.85774,0 372 | 2.2526,9.9636,-3.1749,-2.9944,0 373 | 4.1529,-3.9358,2.8633,-0.017686,0 374 | 0.74307,11.17,-1.3824,-4.0728,0 375 | 1.9105,8.871,-2.3386,-0.75604,0 376 | -1.5055,0.070346,6.8681,-0.50648,0 377 | 0.58836,10.7727,-1.3884,-4.3276,0 378 | 3.2303,7.8384,-3.5348,-1.2151,0 379 | -1.9922,11.6542,2.6542,-5.2107,0 380 | 2.8523,9.0096,-3.761,-3.3371,0 381 | 4.2772,2.4955,0.48554,0.36119,0 382 | 1.5099,0.039307,6.2332,-0.30346,0 383 | 5.4188,10.1457,-4.084,-3.6991,0 384 | 0.86202,2.6963,4.2908,0.54739,0 385 | 3.8117,10.1457,-4.0463,-4.5629,0 386 | 0.54777,10.3754,-1.5435,-4.1633,0 387 | 2.3718,7.4908,0.015989,-1.7414,0 388 | -2.4953,11.1472,1.9353,-3.4638,0 389 | 4.6361,-2.6611,2.8358,1.1991,0 390 | -2.2527,11.5321,2.5899,-3.2737,0 391 | 3.7982,10.423,-4.1602,-4.9728,0 392 | -0.36279,8.2895,-1.9213,-3.3332,0 393 | 2.1265,6.8783,0.44784,-2.2224,0 394 | 0.86736,5.5643,1.6765,-0.16769,0 395 | 3.7831,10.0526,-3.8869,-3.7366,0 396 | -2.2623,12.1177,0.28846,-7.7581,0 397 | 1.2616,4.4303,-1.3335,-1.7517,0 398 | 2.6799,3.1349,0.34073,0.58489,0 399 | -0.39816,5.9781,1.3912,-1.1621,0 400 | 4.3937,0.35798,2.0416,1.2004,0 401 | 2.9695,5.6222,0.27561,-1.1556,0 402 | 1.3049,-0.15521,6.4911,-0.75346,0 403 | 2.2123,-5.8395,7.7687,-0.85302,0 404 | 1.9647,6.9383,0.57722,0.66377,0 405 | 3.0864,-2.5845,2.2309,0.30947,0 406 | 0.3798,0.7098,0.7572,-0.4444,0 407 | 0.58982,7.4266,1.2353,-2.9595,0 408 | 0.14783,7.946,1.0742,-3.3409,0 409 | -0.062025,6.1975,1.099,-1.131,0 410 | 4.223,1.1319,0.72202,0.96118,0 411 | 0.64295,7.1018,0.3493,-0.41337,0 412 | 1.941,0.46351,4.6472,1.0879,0 413 | 4.0047,0.45937,1.3621,1.6181,0 414 | 3.7767,9.7794,-3.9075,-3.5323,0 415 | 3.4769,-0.15314,2.53,2.4495,0 416 | 1.9818,9.2621,-3.521,-1.872,0 417 | 3.8023,-3.8696,4.044,0.95343,0 418 | 4.3483,11.1079,-4.0857,-4.2539,0 419 | 1.1518,1.3864,5.2727,-0.43536,0 420 | -1.2576,1.5892,7.0078,0.42455,0 421 | 1.9572,-5.1153,8.6127,-1.4297,0 422 | -2.484,12.1611,2.8204,-3.7418,0 423 | -1.1497,1.2954,7.701,0.62627,0 424 | 4.8368,10.0132,-4.3239,-4.3276,0 425 | -0.12196,8.8068,0.94566,-4.2267,0 426 | 1.9429,6.3961,0.092248,0.58102,0 427 | 1.742,-4.809,8.2142,-2.0659,0 428 | -1.5222,10.8409,2.7827,-4.0974,0 429 | -1.3,10.2678,-2.953,-5.8638,0 430 | 3.4246,-0.14693,0.80342,0.29136,0 431 | 2.5503,-4.9518,6.3729,-0.41596,0 432 | 1.5691,6.3465,-0.1828,-2.4099,0 433 | 1.3087,4.9228,2.0013,0.22024,0 434 | 5.1776,8.2316,-3.2511,-1.5694,0 435 | 2.229,9.6325,-3.1123,-2.7164,0 436 | 5.6272,10.0857,-4.2931,-3.8142,0 437 | 1.2138,8.7986,-2.1672,-0.74182,0 438 | 0.3798,0.7098,0.7572,-0.4444,0 439 | 0.5415,6.0319,1.6825,-0.46122,0 440 | 4.0524,5.6802,-1.9693,0.026279,0 441 | 4.7285,2.1065,-0.28305,1.5625,0 442 | 3.4359,0.66216,2.1041,1.8922,0 443 | 0.86816,10.2429,-1.4912,-4.0082,0 444 | 3.359,9.8022,-3.8209,-3.7133,0 445 | 3.6702,2.9942,0.85141,0.30688,0 446 | 1.3349,6.1189,0.46497,0.49826,0 447 | 3.1887,-3.4143,2.7742,-0.2026,0 448 | 2.4527,2.9653,0.20021,-0.056479,0 449 | 3.9121,2.9735,0.92852,0.60558,0 450 | 3.9364,10.5885,-3.725,-4.3133,0 451 | 3.9414,-3.2902,3.1674,1.0866,0 452 | 3.6922,-3.9585,4.3439,1.3517,0 453 | 5.681,7.795,-2.6848,-0.92544,0 454 | 0.77124,9.0862,-1.2281,-1.4996,0 455 | 3.5761,9.7753,-3.9795,-3.4638,0 456 | 1.602,6.1251,0.52924,0.47886,0 457 | 2.6682,10.216,-3.4414,-4.0069,0 458 | 2.0007,1.8644,2.6491,0.47369,0 459 | 0.64215,3.1287,4.2933,0.64696,0 460 | 4.3848,-3.0729,3.0423,1.2741,0 461 | 0.77445,9.0552,-2.4089,-1.3884,0 462 | 0.96574,8.393,-1.361,-1.4659,0 463 | 3.0948,8.7324,-2.9007,-0.96682,0 464 | 4.9362,7.6046,-2.3429,-0.85302,0 465 | -1.9458,11.2217,1.9079,-3.4405,0 466 | 5.7403,-0.44284,0.38015,1.3763,0 467 | -2.6989,12.1984,0.67661,-8.5482,0 468 | 1.1472,3.5985,1.9387,-0.43406,0 469 | 2.9742,8.96,-2.9024,-1.0379,0 470 | 4.5707,7.2094,-3.2794,-1.4944,0 471 | 0.1848,6.5079,2.0133,-0.87242,0 472 | 0.87256,9.2931,-0.7843,-2.1978,0 473 | 0.39559,6.8866,1.0588,-0.67587,0 474 | 3.8384,6.1851,-2.0439,-0.033204,0 475 | 2.8209,7.3108,-0.81857,-1.8784,0 476 | 2.5817,9.7546,-3.1749,-2.9957,0 477 | 3.8213,0.23175,2.0133,2.0564,0 478 | 0.3798,0.7098,0.7572,-0.4444,0 479 | 3.4893,6.69,-1.2042,-0.38751,0 480 | -1.7781,0.8546,7.1303,0.027572,0 481 | 2.0962,2.4769,1.9379,-0.040962,0 482 | 0.94732,-0.57113,7.1903,-0.67587,0 483 | 2.8261,9.4007,-3.3034,-1.0509,0 484 | 0.0071249,8.3661,0.50781,-3.8155,0 485 | 0.96788,7.1907,1.2798,-2.4565,0 486 | 4.7432,2.1086,0.1368,1.6543,0 487 | 3.6575,7.2797,-2.2692,-1.144,0 488 | 3.8832,6.4023,-2.432,-0.98363,0 489 | 3.4776,8.811,-3.1886,-0.92285,0 490 | 1.1315,7.9212,1.093,-2.8444,0 491 | 2.8237,2.8597,0.19678,0.57196,0 492 | 1.9321,6.0423,0.26019,-2.053,0 493 | 3.0632,-3.3315,5.1305,0.8267,0 494 | -1.8411,10.8306,2.769,-3.0901,0 495 | 2.8084,11.3045,-3.3394,-4.4194,0 496 | 2.5698,-4.4076,5.9856,0.078002,0 497 | -0.12624,10.3216,-3.7121,-6.1185,0 498 | 3.3756,-4.0951,4.367,1.0698,0 499 | -0.048008,-1.6037,8.4756,0.75558,0 500 | 0.5706,-0.0248,1.2421,-0.5621,0 501 | 0.88444,6.5906,0.55837,-0.44182,0 502 | 3.8644,3.7061,0.70403,0.35214,0 503 | 1.2999,2.5762,2.0107,-0.18967,0 504 | 2.0051,-6.8638,8.132,-0.2401,0 505 | 4.9294,0.27727,0.20792,0.33662,0 506 | 2.8297,6.3485,-0.73546,-0.58665,0 507 | 2.565,8.633,-2.9941,-1.3082,0 508 | 2.093,8.3061,0.022844,-3.2724,0 509 | 4.6014,5.6264,-2.1235,0.19309,0 510 | 5.0617,-0.35799,0.44698,0.99868,0 511 | -0.2951,9.0489,-0.52725,-2.0789,0 512 | 3.577,2.4004,1.8908,0.73231,0 513 | 3.9433,2.5017,1.5215,0.903,0 514 | 2.6648,10.754,-3.3994,-4.1685,0 515 | 5.9374,6.1664,-2.5905,-0.36553,0 516 | 2.0153,1.8479,3.1375,0.42843,0 517 | 5.8782,5.9409,-2.8544,-0.60863,0 518 | -2.3983,12.606,2.9464,-5.7888,0 519 | 1.762,4.3682,2.1384,0.75429,0 520 | 4.2406,-2.4852,1.608,0.7155,0 521 | 3.4669,6.87,-1.0568,-0.73147,0 522 | 3.1896,5.7526,-0.18537,-0.30087,0 523 | 0.81356,9.1566,-2.1492,-4.1814,0 524 | 0.52855,0.96427,4.0243,-1.0483,0 525 | 2.1319,-2.0403,2.5574,-0.061652,0 526 | 0.33111,4.5731,2.057,-0.18967,0 527 | 1.2746,8.8172,-1.5323,-1.7957,0 528 | 2.2091,7.4556,-1.3284,-3.3021,0 529 | 2.5328,7.528,-0.41929,-2.6478,0 530 | 3.6244,1.4609,1.3501,1.9284,0 531 | -1.3885,12.5026,0.69118,-7.5487,0 532 | 5.7227,5.8312,-2.4097,-0.24527,0 533 | 3.3583,10.3567,-3.7301,-3.6991,0 534 | 2.5227,2.2369,2.7236,0.79438,0 535 | 0.045304,6.7334,1.0708,-0.9332,0 536 | 4.8278,7.7598,-2.4491,-1.2216,0 537 | 1.9476,-4.7738,8.527,-1.8668,0 538 | 2.7659,0.66216,4.1494,-0.28406,0 539 | -0.10648,-0.76771,7.7575,0.64179,0 540 | 0.72252,-0.053811,5.6703,-1.3509,0 541 | 4.2475,1.4816,-0.48355,0.95343,0 542 | 3.9772,0.33521,2.2566,2.1625,0 543 | 3.6667,4.302,0.55923,0.33791,0 544 | 2.8232,10.8513,-3.1466,-3.9784,0 545 | -1.4217,11.6542,-0.057699,-7.1025,0 546 | 4.2458,1.1981,0.66633,0.94696,0 547 | 4.1038,-4.8069,3.3491,-0.49225,0 548 | 1.4507,8.7903,-2.2324,-0.65259,0 549 | 3.4647,-3.9172,3.9746,0.36119,0 550 | 1.8533,6.1458,1.0176,-2.0401,0 551 | 3.5288,0.71596,1.9507,1.9375,0 552 | 3.9719,1.0367,0.75973,1.0013,0 553 | 3.534,9.3614,-3.6316,-1.2461,0 554 | 3.6894,9.887,-4.0788,-4.3664,0 555 | 3.0672,-4.4117,3.8238,-0.81682,0 556 | 2.6463,-4.8152,6.3549,0.003003,0 557 | 2.2893,3.733,0.6312,-0.39786,0 558 | 1.5673,7.9274,-0.056842,-2.1694,0 559 | 4.0405,0.51524,1.0279,1.106,0 560 | 4.3846,-4.8794,3.3662,-0.029324,0 561 | 2.0165,-0.25246,5.1707,1.0763,0 562 | 4.0446,11.1741,-4.3582,-4.7401,0 563 | -0.33729,-0.64976,7.6659,0.72326,0 564 | -2.4604,12.7302,0.91738,-7.6418,0 565 | 4.1195,10.9258,-3.8929,-4.1802,0 566 | 2.0193,0.82356,4.6369,1.4202,0 567 | 1.5701,7.9129,0.29018,-2.1953,0 568 | 2.6415,7.586,-0.28562,-1.6677,0 569 | 5.0214,8.0764,-3.0515,-1.7155,0 570 | 4.3435,3.3295,0.83598,0.64955,0 571 | 1.8238,-6.7748,8.3873,-0.54139,0 572 | 3.9382,0.9291,0.78543,0.6767,0 573 | 2.2517,-5.1422,4.2916,-1.2487,0 574 | 5.504,10.3671,-4.413,-4.0211,0 575 | 2.8521,9.171,-3.6461,-1.2047,0 576 | 1.1676,9.1566,-2.0867,-0.80647,0 577 | 2.6104,8.0081,-0.23592,-1.7608,0 578 | 0.32444,10.067,-1.1982,-4.1284,0 579 | 3.8962,-4.7904,3.3954,-0.53751,0 580 | 2.1752,-0.8091,5.1022,-0.67975,0 581 | 1.1588,8.9331,-2.0807,-1.1272,0 582 | 4.7072,8.2957,-2.5605,-1.4905,0 583 | -1.9667,11.8052,-0.40472,-7.8719,0 584 | 4.0552,0.40143,1.4563,0.65343,0 585 | 2.3678,-6.839,8.4207,-0.44829,0 586 | 0.33565,6.8369,0.69718,-0.55691,0 587 | 4.3398,-5.3036,3.8803,-0.70432,0 588 | 1.5456,8.5482,0.4187,-2.1784,0 589 | 1.4276,8.3847,-2.0995,-1.9677,0 590 | -0.27802,8.1881,-3.1338,-2.5276,0 591 | 0.93611,8.6413,-1.6351,-1.3043,0 592 | 4.6352,-3.0087,2.6773,1.212,0 593 | 1.5268,-5.5871,8.6564,-1.722,0 594 | 0.95626,2.4728,4.4578,0.21636,0 595 | -2.7914,1.7734,6.7756,-0.39915,0 596 | 5.2032,3.5116,-1.2538,1.0129,0 597 | 3.1836,7.2321,-1.0713,-2.5909,0 598 | 0.65497,5.1815,1.0673,-0.42113,0 599 | 5.6084,10.3009,-4.8003,-4.3534,0 600 | 1.105,7.4432,0.41099,-3.0332,0 601 | 3.9292,-2.9156,2.2129,0.30817,0 602 | 1.1558,6.4003,1.5506,0.6961,0 603 | 2.5581,2.6218,1.8513,0.40257,0 604 | 2.7831,10.9796,-3.557,-4.4039,0 605 | 3.7635,2.7811,0.66119,0.34179,0 606 | -2.6479,10.1374,-1.331,-5.4707,0 607 | 1.0652,8.3682,-1.4004,-1.6509,0 608 | -1.4275,11.8797,0.41613,-6.9978,0 609 | 5.7456,10.1808,-4.7857,-4.3366,0 610 | 5.086,3.2798,-1.2701,1.1189,0 611 | 3.4092,5.4049,-2.5228,-0.89958,0 612 | -0.2361,9.3221,2.1307,-4.3793,0 613 | 3.8197,8.9951,-4.383,-4.0327,0 614 | -1.1391,1.8127,6.9144,0.70127,0 615 | 4.9249,0.68906,0.77344,1.2095,0 616 | 2.5089,6.841,-0.029423,0.44912,0 617 | -0.2062,9.2207,-3.7044,-6.8103,0 618 | 3.946,6.8514,-1.5443,-0.5582,0 619 | -0.278,8.1881,-3.1338,-2.5276,0 620 | 1.8592,3.2074,-0.15966,-0.26208,0 621 | 0.56953,7.6294,1.5754,-3.2233,0 622 | 3.4626,-4.449,3.5427,0.15429,0 623 | 3.3951,1.1484,2.1401,2.0862,0 624 | 5.0429,-0.52974,0.50439,1.106,0 625 | 3.7758,7.1783,-1.5195,0.40128,0 626 | 4.6562,7.6398,-2.4243,-1.2384,0 627 | 4.0948,-2.9674,2.3689,0.75429,0 628 | 1.8384,6.063,0.54723,0.51248,0 629 | 2.0153,0.43661,4.5864,-0.3151,0 630 | 3.5251,0.7201,1.6928,0.64438,0 631 | 3.757,-5.4236,3.8255,-1.2526,0 632 | 2.5989,3.5178,0.7623,0.81119,0 633 | 1.8994,0.97462,4.2265,0.81377,0 634 | 3.6941,-3.9482,4.2625,1.1577,0 635 | 4.4295,-2.3507,1.7048,0.90946,0 636 | 6.8248,5.2187,-2.5425,0.5461,0 637 | 1.8967,-2.5163,2.8093,-0.79742,0 638 | 2.1526,-6.1665,8.0831,-0.34355,0 639 | 3.3004,7.0811,-1.3258,0.22283,0 640 | 2.7213,7.05,-0.58808,0.41809,0 641 | 3.8846,-3.0336,2.5334,0.20214,0 642 | 4.1665,-0.4449,0.23448,0.27843,0 643 | 0.94225,5.8561,1.8762,-0.32544,0 644 | 5.1321,-0.031048,0.32616,1.1151,0 645 | 0.38251,6.8121,1.8128,-0.61251,0 646 | 3.0333,-2.5928,2.3183,0.303,0 647 | 2.9233,6.0464,-0.11168,-0.58665,0 648 | 1.162,10.2926,-1.2821,-4.0392,0 649 | 3.7791,2.5762,1.3098,0.5655,0 650 | 0.77765,5.9781,1.1941,-0.3526,0 651 | -0.38388,-1.0471,8.0514,0.49567,0 652 | 0.21084,9.4359,-0.094543,-1.859,0 653 | 2.9571,-4.5938,5.9068,0.57196,0 654 | 4.6439,-3.3729,2.5976,0.55257,0 655 | 3.3577,-4.3062,6.0241,0.18274,0 656 | 3.5127,2.9073,1.0579,0.40774,0 657 | 2.6562,10.7044,-3.3085,-4.0767,0 658 | -1.3612,10.694,1.7022,-2.9026,0 659 | -0.278,8.1881,-3.1338,-2.5276,0 660 | 1.04,-6.9321,8.2888,-1.2991,0 661 | 2.1881,2.7356,1.3278,-0.1832,0 662 | 4.2756,-2.6528,2.1375,0.94437,0 663 | -0.11996,6.8741,0.91995,-0.6694,0 664 | 2.9736,8.7944,-3.6359,-1.3754,0 665 | 3.7798,-3.3109,2.6491,0.066365,0 666 | 5.3586,3.7557,-1.7345,1.0789,0 667 | 1.8373,6.1292,0.84027,0.55257,0 668 | 1.2262,0.89599,5.7568,-0.11596,0 669 | -0.048008,-0.56078,7.7215,0.453,0 670 | 0.5706,-0.024841,1.2421,-0.56208,0 671 | 4.3634,0.46351,1.4281,2.0202,0 672 | 3.482,-4.1634,3.5008,-0.078462,0 673 | 0.51947,-3.2633,3.0895,-0.98492,0 674 | 2.3164,-2.628,3.1529,-0.08622,0 675 | -1.8348,11.0334,3.1863,-4.8888,0 676 | 1.3754,8.8793,-1.9136,-0.53751,0 677 | -0.16682,5.8974,0.49839,-0.70044,0 678 | 0.29961,7.1328,-0.31475,-1.1828,0 679 | 0.25035,9.3262,-3.6873,-6.2543,0 680 | 2.4673,1.3926,1.7125,0.41421,0 681 | 0.77805,6.6424,-1.1425,-1.0573,0 682 | 3.4465,2.9508,1.0271,0.5461,0 683 | 2.2429,-4.1427,5.2333,-0.40173,0 684 | 3.7321,-3.884,3.3577,-0.0060486,0 685 | 4.3365,-3.584,3.6884,0.74912,0 686 | -2.0759,10.8223,2.6439,-4.837,0 687 | 4.0715,7.6398,-2.0824,-1.1698,0 688 | 0.76163,5.8209,1.1959,-0.64613,0 689 | -0.53966,7.3273,0.46583,-1.4543,0 690 | 2.6213,5.7919,0.065686,-1.5759,0 691 | 3.0242,-3.3378,2.5865,-0.54785,0 692 | 5.8519,5.3905,-2.4037,-0.061652,0 693 | 0.5706,-0.0248,1.2421,-0.5621,0 694 | 3.9771,11.1513,-3.9272,-4.3444,0 695 | 1.5478,9.1814,-1.6326,-1.7375,0 696 | 0.74054,0.36625,2.1992,0.48403,0 697 | 0.49571,10.2243,-1.097,-4.0159,0 698 | 1.645,7.8612,-0.87598,-3.5569,0 699 | 3.6077,6.8576,-1.1622,0.28231,0 700 | 3.2403,-3.7082,5.2804,0.41291,0 701 | 3.9166,10.2491,-4.0926,-4.4659,0 702 | 3.9262,6.0299,-2.0156,-0.065531,0 703 | 5.591,10.4643,-4.3839,-4.3379,0 704 | 3.7522,-3.6978,3.9943,1.3051,0 705 | 1.3114,4.5462,2.2935,0.22541,0 706 | 3.7022,6.9942,-1.8511,-0.12889,0 707 | 4.364,-3.1039,2.3757,0.78532,0 708 | 3.5829,1.4423,1.0219,1.4008,0 709 | 4.65,-4.8297,3.4553,-0.25174,0 710 | 5.1731,3.9606,-1.983,0.40774,0 711 | 3.2692,3.4184,0.20706,-0.066824,0 712 | 2.4012,1.6223,3.0312,0.71679,0 713 | 1.7257,-4.4697,8.2219,-1.8073,0 714 | 4.7965,6.9859,-1.9967,-0.35001,0 715 | 4.0962,10.1891,-3.9323,-4.1827,0 716 | 2.5559,3.3605,2.0321,0.26809,0 717 | 3.4916,8.5709,-3.0326,-0.59182,0 718 | 0.5195,-3.2633,3.0895,-0.9849,0 719 | 2.9856,7.2673,-0.409,-2.2431,0 720 | 4.0932,5.4132,-1.8219,0.23576,0 721 | 1.7748,-0.76978,5.5854,1.3039,0 722 | 5.2012,0.32694,0.17965,1.1797,0 723 | -0.45062,-1.3678,7.0858,-0.40303,0 724 | 4.8451,8.1116,-2.9512,-1.4724,0 725 | 0.74841,7.2756,1.1504,-0.5388,0 726 | 5.1213,8.5565,-3.3917,-1.5474,0 727 | 3.6181,-3.7454,2.8273,-0.71208,0 728 | 0.040498,8.5234,1.4461,-3.9306,0 729 | -2.6479,10.1374,-1.331,-5.4707,0 730 | 0.37984,0.70975,0.75716,-0.44441,0 731 | -0.95923,0.091039,6.2204,-1.4828,0 732 | 2.8672,10.0008,-3.2049,-3.1095,0 733 | 1.0182,9.109,-0.62064,-1.7129,0 734 | -2.7143,11.4535,2.1092,-3.9629,0 735 | 3.8244,-3.1081,2.4537,0.52024,0 736 | 2.7961,2.121,1.8385,0.38317,0 737 | 3.5358,6.7086,-0.81857,0.47886,0 738 | -0.7056,8.7241,2.2215,-4.5965,0 739 | 4.1542,7.2756,-2.4766,-1.2099,0 740 | 0.92703,9.4318,-0.66263,-1.6728,0 741 | 1.8216,-6.4748,8.0514,-0.41855,0 742 | -2.4473,12.6247,0.73573,-7.6612,0 743 | 3.5862,-3.0957,2.8093,0.24481,0 744 | 0.66191,9.6594,-0.28819,-1.6638,0 745 | 4.7926,1.7071,-0.051701,1.4926,0 746 | 4.9852,8.3516,-2.5425,-1.2823,0 747 | 0.75736,3.0294,2.9164,-0.068117,0 748 | 4.6499,7.6336,-1.9427,-0.37458,0 749 | -0.023579,7.1742,0.78457,-0.75734,0 750 | 0.85574,0.0082678,6.6042,-0.53104,0 751 | 0.88298,0.66009,6.0096,-0.43277,0 752 | 4.0422,-4.391,4.7466,1.137,0 753 | 2.2546,8.0992,-0.24877,-3.2698,0 754 | 0.38478,6.5989,-0.3336,-0.56466,0 755 | 3.1541,-5.1711,6.5991,0.57455,0 756 | 2.3969,0.23589,4.8477,1.437,0 757 | 4.7114,2.0755,-0.2702,1.2379,0 758 | 4.0127,10.1477,-3.9366,-4.0728,0 759 | 2.6606,3.1681,1.9619,0.18662,0 760 | 3.931,1.8541,-0.023425,1.2314,0 761 | 0.01727,8.693,1.3989,-3.9668,0 762 | 3.2414,0.40971,1.4015,1.1952,0 763 | 2.2504,3.5757,0.35273,0.2836,0 764 | -1.3971,3.3191,-1.3927,-1.9948,1 765 | 0.39012,-0.14279,-0.031994,0.35084,1 766 | -1.6677,-7.1535,7.8929,0.96765,1 767 | -3.8483,-12.8047,15.6824,-1.281,1 768 | -3.5681,-8.213,10.083,0.96765,1 769 | -2.2804,-0.30626,1.3347,1.3763,1 770 | -1.7582,2.7397,-2.5323,-2.234,1 771 | -0.89409,3.1991,-1.8219,-2.9452,1 772 | 0.3434,0.12415,-0.28733,0.14654,1 773 | -0.9854,-6.661,5.8245,0.5461,1 774 | -2.4115,-9.1359,9.3444,-0.65259,1 775 | -1.5252,-6.2534,5.3524,0.59912,1 776 | -0.61442,-0.091058,-0.31818,0.50214,1 777 | -0.36506,2.8928,-3.6461,-3.0603,1 778 | -5.9034,6.5679,0.67661,-6.6797,1 779 | -1.8215,2.7521,-0.72261,-2.353,1 780 | -0.77461,-1.8768,2.4023,1.1319,1 781 | -1.8187,-9.0366,9.0162,-0.12243,1 782 | -3.5801,-12.9309,13.1779,-2.5677,1 783 | -1.8219,-6.8824,5.4681,0.057313,1 784 | -0.3481,-0.38696,-0.47841,0.62627,1 785 | 0.47368,3.3605,-4.5064,-4.0431,1 786 | -3.4083,4.8587,-0.76888,-4.8668,1 787 | -1.6662,-0.30005,1.4238,0.024986,1 788 | -2.0962,-7.1059,6.6188,-0.33708,1 789 | -2.6685,-10.4519,9.1139,-1.7323,1 790 | -0.47465,-4.3496,1.9901,0.7517,1 791 | 1.0552,1.1857,-2.6411,0.11033,1 792 | 1.1644,3.8095,-4.9408,-4.0909,1 793 | -4.4779,7.3708,-0.31218,-6.7754,1 794 | -2.7338,0.45523,2.4391,0.21766,1 795 | -2.286,-5.4484,5.8039,0.88231,1 796 | -1.6244,-6.3444,4.6575,0.16981,1 797 | 0.50813,0.47799,-1.9804,0.57714,1 798 | 1.6408,4.2503,-4.9023,-2.6621,1 799 | 0.81583,4.84,-5.2613,-6.0823,1 800 | -5.4901,9.1048,-0.38758,-5.9763,1 801 | -3.2238,2.7935,0.32274,-0.86078,1 802 | -2.0631,-1.5147,1.219,0.44524,1 803 | -0.91318,-2.0113,-0.19565,0.066365,1 804 | 0.6005,1.9327,-3.2888,-0.32415,1 805 | 0.91315,3.3377,-4.0557,-1.6741,1 806 | -0.28015,3.0729,-3.3857,-2.9155,1 807 | -3.6085,3.3253,-0.51954,-3.5737,1 808 | -6.2003,8.6806,0.0091344,-3.703,1 809 | -4.2932,3.3419,0.77258,-0.99785,1 810 | -3.0265,-0.062088,0.68604,-0.055186,1 811 | -1.7015,-0.010356,-0.99337,-0.53104,1 812 | -0.64326,2.4748,-2.9452,-1.0276,1 813 | -0.86339,1.9348,-2.3729,-1.0897,1 814 | -2.0659,1.0512,-0.46298,-1.0974,1 815 | -2.1333,1.5685,-0.084261,-1.7453,1 816 | -1.2568,-1.4733,2.8718,0.44653,1 817 | -3.1128,-6.841,10.7402,-1.0172,1 818 | -4.8554,-5.9037,10.9818,-0.82199,1 819 | -2.588,3.8654,-0.3336,-1.2797,1 820 | 0.24394,1.4733,-1.4192,-0.58535,1 821 | -1.5322,-5.0966,6.6779,0.17498,1 822 | -4.0025,-13.4979,17.6772,-3.3202,1 823 | -4.0173,-8.3123,12.4547,-1.4375,1 824 | -3.0731,-0.53181,2.3877,0.77627,1 825 | -1.979,3.2301,-1.3575,-2.5819,1 826 | -0.4294,-0.14693,0.044265,-0.15605,1 827 | -2.234,-7.0314,7.4936,0.61334,1 828 | -4.211,-12.4736,14.9704,-1.3884,1 829 | -3.8073,-8.0971,10.1772,0.65084,1 830 | -2.5912,-0.10554,1.2798,1.0414,1 831 | -2.2482,3.0915,-2.3969,-2.6711,1 832 | -1.4427,3.2922,-1.9702,-3.4392,1 833 | -0.39416,-0.020702,-0.066267,-0.44699,1 834 | -1.522,-6.6383,5.7491,-0.10691,1 835 | -2.8267,-9.0407,9.0694,-0.98233,1 836 | -1.7263,-6.0237,5.2419,0.29524,1 837 | -0.94255,0.039307,-0.24192,0.31593,1 838 | -0.89569,3.0025,-3.6067,-3.4457,1 839 | -6.2815,6.6651,0.52581,-7.0107,1 840 | -2.3211,3.166,-1.0002,-2.7151,1 841 | -1.3414,-2.0776,2.8093,0.60688,1 842 | -2.258,-9.3263,9.3727,-0.85949,1 843 | -3.8858,-12.8461,12.7957,-3.1353,1 844 | -1.8969,-6.7893,5.2761,-0.32544,1 845 | -0.52645,-0.24832,-0.45613,0.41938,1 846 | 0.0096613,3.5612,-4.407,-4.4103,1 847 | -3.8826,4.898,-0.92311,-5.0801,1 848 | -2.1405,-0.16762,1.321,-0.20906,1 849 | -2.4824,-7.3046,6.839,-0.59053,1 850 | -2.9098,-10.0712,8.4156,-1.9948,1 851 | -0.60975,-4.002,1.8471,0.6017,1 852 | 0.83625,1.1071,-2.4706,-0.062945,1 853 | 0.60731,3.9544,-4.772,-4.4853,1 854 | -4.8861,7.0542,-0.17252,-6.959,1 855 | -3.1366,0.42212,2.6225,-0.064238,1 856 | -2.5754,-5.6574,6.103,0.65214,1 857 | -1.8782,-6.5865,4.8486,-0.021566,1 858 | 0.24261,0.57318,-1.9402,0.44007,1 859 | 1.296,4.2855,-4.8457,-2.9013,1 860 | 0.25943,5.0097,-5.0394,-6.3862,1 861 | -5.873,9.1752,-0.27448,-6.0422,1 862 | -3.4605,2.6901,0.16165,-1.0224,1 863 | -2.3797,-1.4402,1.1273,0.16076,1 864 | -1.2424,-1.7175,-0.52553,-0.21036,1 865 | 0.20216,1.9182,-3.2828,-0.61768,1 866 | 0.59823,3.5012,-3.9795,-1.7841,1 867 | -0.77995,3.2322,-3.282,-3.1004,1 868 | -4.1409,3.4619,-0.47841,-3.8879,1 869 | -6.5084,8.7696,0.23191,-3.937,1 870 | -4.4996,3.4288,0.56265,-1.1672,1 871 | -3.3125,0.10139,0.55323,-0.2957,1 872 | -1.9423,0.3766,-1.2898,-0.82458,1 873 | -0.75793,2.5349,-3.0464,-1.2629,1 874 | -0.95403,1.9824,-2.3163,-1.1957,1 875 | -2.2173,1.4671,-0.72689,-1.1724,1 876 | -2.799,1.9679,-0.42357,-2.1125,1 877 | -1.8629,-0.84841,2.5377,0.097399,1 878 | -3.5916,-6.2285,10.2389,-1.1543,1 879 | -5.1216,-5.3118,10.3846,-1.0612,1 880 | -3.2854,4.0372,-0.45356,-1.8228,1 881 | -0.56877,1.4174,-1.4252,-1.1246,1 882 | -2.3518,-4.8359,6.6479,-0.060358,1 883 | -4.4861,-13.2889,17.3087,-3.2194,1 884 | -4.3876,-7.7267,11.9655,-1.4543,1 885 | -3.3604,-0.32696,2.1324,0.6017,1 886 | -1.0112,2.9984,-1.1664,-1.6185,1 887 | 0.030219,-1.0512,1.4024,0.77369,1 888 | -1.6514,-8.4985,9.1122,1.2379,1 889 | -3.2692,-12.7406,15.5573,-0.14182,1 890 | -2.5701,-6.8452,8.9999,2.1353,1 891 | -1.3066,0.25244,0.7623,1.7758,1 892 | -1.6637,3.2881,-2.2701,-2.2224,1 893 | -0.55008,2.8659,-1.6488,-2.4319,1 894 | 0.21431,-0.69529,0.87711,0.29653,1 895 | -0.77288,-7.4473,6.492,0.36119,1 896 | -1.8391,-9.0883,9.2416,-0.10432,1 897 | -0.63298,-5.1277,4.5624,1.4797,1 898 | 0.0040545,0.62905,-0.64121,0.75817,1 899 | -0.28696,3.1784,-3.5767,-3.1896,1 900 | -5.2406,6.6258,-0.19908,-6.8607,1 901 | -1.4446,2.1438,-0.47241,-1.6677,1 902 | -0.65767,-2.8018,3.7115,0.99739,1 903 | -1.5449,-10.1498,9.6152,-1.2332,1 904 | -2.8957,-12.0205,11.9149,-2.7552,1 905 | -0.81479,-5.7381,4.3919,0.3211,1 906 | 0.50225,0.65388,-1.1793,0.39998,1 907 | 0.74521,3.6357,-4.4044,-4.1414,1 908 | -2.9146,4.0537,-0.45699,-4.0327,1 909 | -1.3907,-1.3781,2.3055,-0.021566,1 910 | -1.786,-8.1157,7.0858,-1.2112,1 911 | -1.7322,-9.2828,7.719,-1.7168,1 912 | 0.55298,-3.4619,1.7048,1.1008,1 913 | 2.031,1.852,-3.0121,0.003003,1 914 | 1.2279,4.0309,-4.6435,-3.9125,1 915 | -4.2249,6.2699,0.15822,-5.5457,1 916 | -2.5346,-0.77392,3.3602,0.00171,1 917 | -1.749,-6.332,6.0987,0.14266,1 918 | -0.539,-5.167,3.4399,0.052141,1 919 | 1.5631,0.89599,-1.9702,0.65472,1 920 | 2.3917,4.5565,-4.9888,-2.8987,1 921 | 0.89512,4.7738,-4.8431,-5.5909,1 922 | -5.4808,8.1819,0.27818,-5.0323,1 923 | -2.8833,1.7713,0.68946,-0.4638,1 924 | -1.4174,-2.2535,1.518,0.61981,1 925 | 0.4283,-0.94981,-1.0731,0.3211,1 926 | 1.5904,2.2121,-3.1183,-0.11725,1 927 | 1.7425,3.6833,-4.0129,-1.7207,1 928 | -0.23356,3.2405,-3.0669,-2.7784,1 929 | -3.6227,3.9958,-0.35845,-3.9047,1 930 | -6.1536,7.9295,0.61663,-3.2646,1 931 | -3.9172,2.6652,0.78886,-0.7819,1 932 | -2.2214,-0.23798,0.56008,0.05602,1 933 | -0.49241,0.89392,-1.6283,-0.56854,1 934 | 0.26517,2.4066,-2.8416,-0.59958,1 935 | -0.10234,1.8189,-2.2169,-0.56725,1 936 | -1.6176,1.0926,-0.35502,-0.59958,1 937 | -1.8448,1.254,0.27218,-1.0728,1 938 | -1.2786,-2.4087,4.5735,0.47627,1 939 | -2.902,-7.6563,11.8318,-0.84268,1 940 | -4.3773,-5.5167,10.939,-0.4082,1 941 | -2.0529,3.8385,-0.79544,-1.2138,1 942 | 0.18868,0.70148,-0.51182,0.0055892,1 943 | -1.7279,-6.841,8.9494,0.68058,1 944 | -3.3793,-13.7731,17.9274,-2.0323,1 945 | -3.1273,-7.1121,11.3897,-0.083634,1 946 | -2.121,-0.05588,1.949,1.353,1 947 | -1.7697,3.4329,-1.2144,-2.3789,1 948 | -0.0012852,0.13863,-0.19651,0.0081754,1 949 | -1.682,-6.8121,7.1398,1.3323,1 950 | -3.4917,-12.1736,14.3689,-0.61639,1 951 | -3.1158,-8.6289,10.4403,0.97153,1 952 | -2.0891,-0.48422,1.704,1.7435,1 953 | -1.6936,2.7852,-2.1835,-1.9276,1 954 | -1.2846,3.2715,-1.7671,-3.2608,1 955 | -0.092194,0.39315,-0.32846,-0.13794,1 956 | -1.0292,-6.3879,5.5255,0.79955,1 957 | -2.2083,-9.1069,8.9991,-0.28406,1 958 | -1.0744,-6.3113,5.355,0.80472,1 959 | -0.51003,-0.23591,0.020273,0.76334,1 960 | -0.36372,3.0439,-3.4816,-2.7836,1 961 | -6.3979,6.4479,1.0836,-6.6176,1 962 | -2.2501,3.3129,-0.88369,-2.8974,1 963 | -1.1859,-1.2519,2.2635,0.77239,1 964 | -1.8076,-8.8131,8.7086,-0.21682,1 965 | -3.3863,-12.9889,13.0545,-2.7202,1 966 | -1.4106,-7.108,5.6454,0.31335,1 967 | -0.21394,-0.68287,0.096532,1.1965,1 968 | 0.48797,3.5674,-4.3882,-3.8116,1 969 | -3.8167,5.1401,-0.65063,-5.4306,1 970 | -1.9555,0.20692,1.2473,-0.3707,1 971 | -2.1786,-6.4479,6.0344,-0.20777,1 972 | -2.3299,-9.9532,8.4756,-1.8733,1 973 | 0.0031201,-4.0061,1.7956,0.91722,1 974 | 1.3518,1.0595,-2.3437,0.39998,1 975 | 1.2309,3.8923,-4.8277,-4.0069,1 976 | -5.0301,7.5032,-0.13396,-7.5034,1 977 | -3.0799,0.60836,2.7039,-0.23751,1 978 | -2.2987,-5.227,5.63,0.91722,1 979 | -1.239,-6.541,4.8151,-0.033204,1 980 | 0.75896,0.29176,-1.6506,0.83834,1 981 | 1.6799,4.2068,-4.5398,-2.3931,1 982 | 0.63655,5.2022,-5.2159,-6.1211,1 983 | -6.0598,9.2952,-0.43642,-6.3694,1 984 | -3.518,2.8763,0.1548,-1.2086,1 985 | -2.0336,-1.4092,1.1582,0.36507,1 986 | -0.69745,-1.7672,-0.34474,-0.12372,1 987 | 0.75108,1.9161,-3.1098,-0.20518,1 988 | 0.84546,3.4826,-3.6307,-1.3961,1 989 | -0.55648,3.2136,-3.3085,-2.7965,1 990 | -3.6817,3.2239,-0.69347,-3.4004,1 991 | -6.7526,8.8172,-0.061983,-3.725,1 992 | -4.577,3.4515,0.66719,-0.94742,1 993 | -2.9883,0.31245,0.45041,0.068951,1 994 | -1.4781,0.14277,-1.1622,-0.48579,1 995 | -0.46651,2.3383,-2.9812,-1.0431,1 996 | -0.8734,1.6533,-2.1964,-0.78061,1 997 | -2.1234,1.1815,-0.55552,-0.81165,1 998 | -2.3142,2.0838,-0.46813,-1.6767,1 999 | -1.4233,-0.98912,2.3586,0.39481,1 1000 | -3.0866,-6.6362,10.5405,-0.89182,1 1001 | -4.7331,-6.1789,11.388,-1.0741,1 1002 | -2.8829,3.8964,-0.1888,-1.1672,1 1003 | -0.036127,1.525,-1.4089,-0.76121,1 1004 | -1.7104,-4.778,6.2109,0.3974,1 1005 | -3.8203,-13.0551,16.9583,-2.3052,1 1006 | -3.7181,-8.5089,12.363,-0.95518,1 1007 | -2.899,-0.60424,2.6045,1.3776,1 1008 | -0.98193,2.7956,-1.2341,-1.5668,1 1009 | -0.17296,-1.1816,1.3818,0.7336,1 1010 | -1.9409,-8.6848,9.155,0.94049,1 1011 | -3.5713,-12.4922,14.8881,-0.47027,1 1012 | -2.9915,-6.6258,8.6521,1.8198,1 1013 | -1.8483,0.31038,0.77344,1.4189,1 1014 | -2.2677,3.2964,-2.2563,-2.4642,1 1015 | -0.50816,2.868,-1.8108,-2.2612,1 1016 | 0.14329,-1.0885,1.0039,0.48791,1 1017 | -0.90784,-7.9026,6.7807,0.34179,1 1018 | -2.0042,-9.3676,9.3333,-0.10303,1 1019 | -0.93587,-5.1008,4.5367,1.3866,1 1020 | -0.40804,0.54214,-0.52725,0.6586,1 1021 | -0.8172,3.3812,-3.6684,-3.456,1 1022 | -4.8392,6.6755,-0.24278,-6.5775,1 1023 | -1.2792,2.1376,-0.47584,-1.3974,1 1024 | -0.66008,-3.226,3.8058,1.1836,1 1025 | -1.7713,-10.7665,10.2184,-1.0043,1 1026 | -3.0061,-12.2377,11.9552,-2.1603,1 1027 | -1.1022,-5.8395,4.5641,0.68705,1 1028 | 0.11806,0.39108,-0.98223,0.42843,1 1029 | 0.11686,3.735,-4.4379,-4.3741,1 1030 | -2.7264,3.9213,-0.49212,-3.6371,1 1031 | -1.2369,-1.6906,2.518,0.51636,1 1032 | -1.8439,-8.6475,7.6796,-0.66682,1 1033 | -1.8554,-9.6035,7.7764,-0.97716,1 1034 | 0.16358,-3.3584,1.3749,1.3569,1 1035 | 1.5077,1.9596,-3.0584,-0.12243,1 1036 | 0.67886,4.1199,-4.569,-4.1414,1 1037 | -3.9934,5.8333,0.54723,-4.9379,1 1038 | -2.3898,-0.78427,3.0141,0.76205,1 1039 | -1.7976,-6.7686,6.6753,0.89912,1 1040 | -0.70867,-5.5602,4.0483,0.903,1 1041 | 1.0194,1.1029,-2.3,0.59395,1 1042 | 1.7875,4.78,-5.1362,-3.2362,1 1043 | 0.27331,4.8773,-4.9194,-5.8198,1 1044 | -5.1661,8.0433,0.044265,-4.4983,1 1045 | -2.7028,1.6327,0.83598,-0.091393,1 1046 | -1.4904,-2.2183,1.6054,0.89394,1 1047 | -0.014902,-1.0243,-0.94024,0.64955,1 1048 | 0.88992,2.2638,-3.1046,-0.11855,1 1049 | 1.0637,3.6957,-4.1594,-1.9379,1 1050 | -0.8471,3.1329,-3.0112,-2.9388,1 1051 | -3.9594,4.0289,-0.35845,-3.8957,1 1052 | -5.8818,7.6584,0.5558,-2.9155,1 1053 | -3.7747,2.5162,0.83341,-0.30993,1 1054 | -2.4198,-0.24418,0.70146,0.41809,1 1055 | -0.83535,0.80494,-1.6411,-0.19225,1 1056 | -0.30432,2.6528,-2.7756,-0.65647,1 1057 | -0.60254,1.7237,-2.1501,-0.77027,1 1058 | -2.1059,1.1815,-0.53324,-0.82716,1 1059 | -2.0441,1.2271,0.18564,-1.091,1 1060 | -1.5621,-2.2121,4.2591,0.27972,1 1061 | -3.2305,-7.2135,11.6433,-0.94613,1 1062 | -4.8426,-4.9932,10.4052,-0.53104,1 1063 | -2.3147,3.6668,-0.6969,-1.2474,1 1064 | -0.11716,0.60422,-0.38587,-0.059065,1 1065 | -2.0066,-6.719,9.0162,0.099985,1 1066 | -3.6961,-13.6779,17.5795,-2.6181,1 1067 | -3.6012,-6.5389,10.5234,-0.48967,1 1068 | -2.6286,0.18002,1.7956,0.97282,1 1069 | -0.82601,2.9611,-1.2864,-1.4647,1 1070 | 0.31803,-0.99326,1.0947,0.88619,1 1071 | -1.4454,-8.4385,8.8483,0.96894,1 1072 | -3.1423,-13.0365,15.6773,-0.66165,1 1073 | -2.5373,-6.959,8.8054,1.5289,1 1074 | -1.366,0.18416,0.90539,1.5806,1 1075 | -1.7064,3.3088,-2.2829,-2.1978,1 1076 | -0.41965,2.9094,-1.7859,-2.2069,1 1077 | 0.37637,-0.82358,0.78543,0.74524,1 1078 | -0.55355,-7.9233,6.7156,0.74394,1 1079 | -1.6001,-9.5828,9.4044,0.081882,1 1080 | -0.37013,-5.554,4.7749,1.547,1 1081 | 0.12126,0.22347,-0.47327,0.97024,1 1082 | -0.27068,3.2674,-3.5562,-3.0888,1 1083 | -5.119,6.6486,-0.049987,-6.5206,1 1084 | -1.3946,2.3134,-0.44499,-1.4905,1 1085 | -0.69879,-3.3771,4.1211,1.5043,1 1086 | -1.48,-10.5244,9.9176,-0.5026,1 1087 | -2.6649,-12.813,12.6689,-1.9082,1 1088 | -0.62684,-6.301,4.7843,1.106,1 1089 | 0.518,0.25865,-0.84085,0.96118,1 1090 | 0.64376,3.764,-4.4738,-4.0483,1 1091 | -2.9821,4.1986,-0.5898,-3.9642,1 1092 | -1.4628,-1.5706,2.4357,0.49826,1 1093 | -1.7101,-8.7903,7.9735,-0.45475,1 1094 | -1.5572,-9.8808,8.1088,-1.0806,1 1095 | 0.74428,-3.7723,1.6131,1.5754,1 1096 | 2.0177,1.7982,-2.9581,0.2099,1 1097 | 1.164,3.913,-4.5544,-3.8672,1 1098 | -4.3667,6.0692,0.57208,-5.4668,1 1099 | -2.5919,-1.0553,3.8949,0.77757,1 1100 | -1.8046,-6.8141,6.7019,1.1681,1 1101 | -0.71868,-5.7154,3.8298,1.0233,1 1102 | 1.4378,0.66837,-2.0267,1.0271,1 1103 | 2.1943,4.5503,-4.976,-2.7254,1 1104 | 0.7376,4.8525,-4.7986,-5.6659,1 1105 | -5.637,8.1261,0.13081,-5.0142,1 1106 | -3.0193,1.7775,0.73745,-0.45346,1 1107 | -1.6706,-2.09,1.584,0.71162,1 1108 | -0.1269,-1.1505,-0.95138,0.57843,1 1109 | 1.2198,2.0982,-3.1954,0.12843,1 1110 | 1.4501,3.6067,-4.0557,-1.5966,1 1111 | -0.40857,3.0977,-2.9607,-2.6892,1 1112 | -3.8952,3.8157,-0.31304,-3.8194,1 1113 | -6.3679,8.0102,0.4247,-3.2207,1 1114 | -4.1429,2.7749,0.68261,-0.71984,1 1115 | -2.6864,-0.097265,0.61663,0.061192,1 1116 | -1.0555,0.79459,-1.6968,-0.46768,1 1117 | -0.29858,2.4769,-2.9512,-0.66165,1 1118 | -0.49948,1.7734,-2.2469,-0.68104,1 1119 | -1.9881,0.99945,-0.28562,-0.70044,1 1120 | -1.9389,1.5706,0.045979,-1.122,1 1121 | -1.4375,-1.8624,4.026,0.55127,1 1122 | -3.1875,-7.5756,11.8678,-0.57889,1 1123 | -4.6765,-5.6636,10.969,-0.33449,1 1124 | -2.0285,3.8468,-0.63435,-1.175,1 1125 | 0.26637,0.73252,-0.67891,0.03533,1 1126 | -1.7589,-6.4624,8.4773,0.31981,1 1127 | -3.5985,-13.6593,17.6052,-2.4927,1 1128 | -3.3582,-7.2404,11.4419,-0.57113,1 1129 | -2.3629,-0.10554,1.9336,1.1358,1 1130 | -2.1802,3.3791,-1.2256,-2.6621,1 1131 | -0.40951,-0.15521,0.060545,-0.088807,1 1132 | -2.2918,-7.257,7.9597,0.9211,1 1133 | -4.0214,-12.8006,15.6199,-0.95647,1 1134 | -3.3884,-8.215,10.3315,0.98187,1 1135 | -2.0046,-0.49457,1.333,1.6543,1 1136 | -1.7063,2.7956,-2.378,-2.3491,1 1137 | -1.6386,3.3584,-1.7302,-3.5646,1 1138 | -0.41645,0.32487,-0.33617,-0.36036,1 1139 | -1.5877,-6.6072,5.8022,0.31593,1 1140 | -2.5961,-9.349,9.7942,-0.28018,1 1141 | -1.5228,-6.4789,5.7568,0.87325,1 1142 | -0.53072,-0.097265,-0.21793,1.0426,1 1143 | -0.49081,2.8452,-3.6436,-3.1004,1 1144 | -6.5773,6.8017,0.85483,-7.5344,1 1145 | -2.4621,2.7645,-0.62578,-2.8573,1 1146 | -1.3995,-1.9162,2.5154,0.59912,1 1147 | -2.3221,-9.3304,9.233,-0.79871,1 1148 | -3.73,-12.9723,12.9817,-2.684,1 1149 | -1.6988,-7.1163,5.7902,0.16723,1 1150 | -0.26654,-0.64562,-0.42014,0.89136,1 1151 | 0.33325,3.3108,-4.5081,-4.012,1 1152 | -4.2091,4.7283,-0.49126,-5.2159,1 1153 | -2.3142,-0.68494,1.9833,-0.44829,1 1154 | -2.4835,-7.4494,6.8964,-0.64484,1 1155 | -2.7611,-10.5099,9.0239,-1.9547,1 1156 | -0.36025,-4.449,2.1067,0.94308,1 1157 | 1.0117,0.9022,-2.3506,0.42714,1 1158 | 0.96708,3.8426,-4.9314,-4.1323,1 1159 | -5.2049,7.259,0.070827,-7.3004,1 1160 | -3.3203,-0.02691,2.9618,-0.44958,1 1161 | -2.565,-5.7899,6.0122,0.046968,1 1162 | -1.5951,-6.572,4.7689,-0.94354,1 1163 | 0.7049,0.17174,-1.7859,0.36119,1 1164 | 1.7331,3.9544,-4.7412,-2.5017,1 1165 | 0.6818,4.8504,-5.2133,-6.1043,1 1166 | -6.3364,9.2848,0.014275,-6.7844,1 1167 | -3.8053,2.4273,0.6809,-1.0871,1 1168 | -2.1979,-2.1252,1.7151,0.45171,1 1169 | -0.87874,-2.2121,-0.051701,0.099985,1 1170 | 0.74067,1.7299,-3.1963,-0.1457,1 1171 | 0.98296,3.4226,-3.9692,-1.7116,1 1172 | -0.3489,3.1929,-3.4054,-3.1832,1 1173 | -3.8552,3.5219,-0.38415,-3.8608,1 1174 | -6.9599,8.9931,0.2182,-4.572,1 1175 | -4.7462,3.1205,1.075,-1.2966,1 1176 | -3.2051,-0.14279,0.97565,0.045675,1 1177 | -1.7549,-0.080711,-0.75774,-0.3707,1 1178 | -0.59587,2.4811,-2.8673,-0.89828,1 1179 | -0.89542,2.0279,-2.3652,-1.2746,1 1180 | -2.0754,1.2767,-0.64206,-1.2642,1 1181 | -3.2778,1.8023,0.1805,-2.3931,1 1182 | -2.2183,-1.254,2.9986,0.36378,1 1183 | -3.5895,-6.572,10.5251,-0.16381,1 1184 | -5.0477,-5.8023,11.244,-0.3901,1 1185 | -3.5741,3.944,-0.07912,-2.1203,1 1186 | -0.7351,1.7361,-1.4938,-1.1582,1 1187 | -2.2617,-4.7428,6.3489,0.11162,1 1188 | -4.244,-13.0634,17.1116,-2.8017,1 1189 | -4.0218,-8.304,12.555,-1.5099,1 1190 | -3.0201,-0.67253,2.7056,0.85774,1 1191 | -2.4941,3.5447,-1.3721,-2.8483,1 1192 | -0.83121,0.039307,0.05369,-0.23105,1 1193 | -2.5665,-6.8824,7.5416,0.70774,1 1194 | -4.4018,-12.9371,15.6559,-1.6806,1 1195 | -3.7573,-8.2916,10.3032,0.38059,1 1196 | -2.4725,-0.40145,1.4855,1.1189,1 1197 | -1.9725,2.8825,-2.3086,-2.3724,1 1198 | -2.0149,3.6874,-1.9385,-3.8918,1 1199 | -0.82053,0.65181,-0.48869,-0.52716,1 1200 | -1.7886,-6.3486,5.6154,0.42584,1 1201 | -2.9138,-9.4711,9.7668,-0.60216,1 1202 | -1.8343,-6.5907,5.6429,0.54998,1 1203 | -0.8734,-0.033118,-0.20165,0.55774,1 1204 | -0.70346,2.957,-3.5947,-3.1457,1 1205 | -6.7387,6.9879,0.67833,-7.5887,1 1206 | -2.7723,3.2777,-0.9351,-3.1457,1 1207 | -1.6641,-1.3678,1.997,0.52283,1 1208 | -2.4349,-9.2497,8.9922,-0.50001,1 1209 | -3.793,-12.7095,12.7957,-2.825,1 1210 | -1.9551,-6.9756,5.5383,-0.12889,1 1211 | -0.69078,-0.50077,-0.35417,0.47498,1 1212 | 0.025013,3.3998,-4.4327,-4.2655,1 1213 | -4.3967,4.9601,-0.64892,-5.4719,1 1214 | -2.456,-0.24418,1.4041,-0.45863,1 1215 | -2.62,-6.8555,6.2169,-0.62285,1 1216 | -2.9662,-10.3257,8.784,-2.1138,1 1217 | -0.71494,-4.4448,2.2241,0.49826,1 1218 | 0.6005,0.99945,-2.2126,0.097399,1 1219 | 0.61652,3.8944,-4.7275,-4.3948,1 1220 | -5.4414,7.2363,0.10938,-7.5642,1 1221 | -3.5798,0.45937,2.3457,-0.45734,1 1222 | -2.7769,-5.6967,5.9179,0.37671,1 1223 | -1.8356,-6.7562,5.0585,-0.55044,1 1224 | 0.30081,0.17381,-1.7542,0.48921,1 1225 | 1.3403,4.1323,-4.7018,-2.5987,1 1226 | 0.26877,4.987,-5.1508,-6.3913,1 1227 | -6.5235,9.6014,-0.25392,-6.9642,1 1228 | -4.0679,2.4955,0.79571,-1.1039,1 1229 | -2.564,-1.7051,1.5026,0.32757,1 1230 | -1.3414,-1.9162,-0.15538,-0.11984,1 1231 | 0.23874,2.0879,-3.3522,-0.66553,1 1232 | 0.6212,3.6771,-4.0771,-2.0711,1 1233 | -0.77848,3.4019,-3.4859,-3.5569,1 1234 | -4.1244,3.7909,-0.6532,-4.1802,1 1235 | -7.0421,9.2,0.25933,-4.6832,1 1236 | -4.9462,3.5716,0.82742,-1.4957,1 1237 | -3.5359,0.30417,0.6569,-0.2957,1 1238 | -2.0662,0.16967,-1.0054,-0.82975,1 1239 | -0.88728,2.808,-3.1432,-1.2035,1 1240 | -1.0941,2.3072,-2.5237,-1.4453,1 1241 | -2.4458,1.6285,-0.88541,-1.4802,1 1242 | -3.551,1.8955,0.1865,-2.4409,1 1243 | -2.2811,-0.85669,2.7185,0.044382,1 1244 | -3.6053,-5.974,10.0916,-0.82846,1 1245 | -5.0676,-5.1877,10.4266,-0.86725,1 1246 | -3.9204,4.0723,-0.23678,-2.1151,1 1247 | -1.1306,1.8458,-1.3575,-1.3806,1 1248 | -2.4561,-4.5566,6.4534,-0.056479,1 1249 | -4.4775,-13.0303,17.0834,-3.0345,1 1250 | -4.1958,-8.1819,12.1291,-1.6017,1 1251 | -3.38,-0.7077,2.5325,0.71808,1 1252 | -2.4365,3.6026,-1.4166,-2.8948,1 1253 | -0.77688,0.13036,-0.031137,-0.35389,1 1254 | -2.7083,-6.8266,7.5339,0.59007,1 1255 | -4.5531,-12.5854,15.4417,-1.4983,1 1256 | -3.8894,-7.8322,9.8208,0.47498,1 1257 | -2.5084,-0.22763,1.488,1.2069,1 1258 | -2.1652,3.0211,-2.4132,-2.4241,1 1259 | -1.8974,3.5074,-1.7842,-3.8491,1 1260 | -0.62043,0.5587,-0.38587,-0.66423,1 1261 | -1.8387,-6.301,5.6506,0.19567,1 1262 | -3,-9.1566,9.5766,-0.73018,1 1263 | -1.9116,-6.1603,5.606,0.48533,1 1264 | -1.005,0.084831,-0.2462,0.45688,1 1265 | -0.87834,3.257,-3.6778,-3.2944,1 1266 | -6.651,6.7934,0.68604,-7.5887,1 1267 | -2.5463,3.1101,-0.83228,-3.0358,1 1268 | -1.4377,-1.432,2.1144,0.42067,1 1269 | -2.4554,-9.0407,8.862,-0.86983,1 1270 | -3.9411,-12.8792,13.0597,-3.3125,1 1271 | -2.1241,-6.8969,5.5992,-0.47156,1 1272 | -0.74324,-0.32902,-0.42785,0.23317,1 1273 | -0.071503,3.7412,-4.5415,-4.2526,1 1274 | -4.2333,4.9166,-0.49212,-5.3207,1 1275 | -2.3675,-0.43663,1.692,-0.43018,1 1276 | -2.5526,-7.3625,6.9255,-0.66811,1 1277 | -3.0986,-10.4602,8.9717,-2.3427,1 1278 | -0.89809,-4.4862,2.2009,0.50731,1 1279 | 0.56232,1.0015,-2.2726,-0.0060486,1 1280 | 0.53936,3.8944,-4.8166,-4.3418,1 1281 | -5.3012,7.3915,0.029699,-7.3987,1 1282 | -3.3553,0.35591,2.6473,-0.37846,1 1283 | -2.7908,-5.7133,5.953,0.45946,1 1284 | -1.9983,-6.6072,4.8254,-0.41984,1 1285 | 0.15423,0.11794,-1.6823,0.59524,1 1286 | 1.208,4.0744,-4.7635,-2.6129,1 1287 | 0.2952,4.8856,-5.149,-6.2323,1 1288 | -6.4247,9.5311,0.022844,-6.8517,1 1289 | -3.9933,2.6218,0.62863,-1.1595,1 1290 | -2.659,-1.6058,1.3647,0.16464,1 1291 | -1.4094,-2.1252,-0.10397,-0.19225,1 1292 | 0.11032,1.9741,-3.3668,-0.65259,1 1293 | 0.52374,3.644,-4.0746,-1.9909,1 1294 | -0.76794,3.4598,-3.4405,-3.4276,1 1295 | -3.9698,3.6812,-0.60008,-4.0133,1 1296 | -7.0364,9.2931,0.16594,-4.5396,1 1297 | -4.9447,3.3005,1.063,-1.444,1 1298 | -3.5933,0.22968,0.7126,-0.3332,1 1299 | -2.1674,0.12415,-1.0465,-0.86208,1 1300 | -0.9607,2.6963,-3.1226,-1.3121,1 1301 | -1.0802,2.1996,-2.5862,-1.2759,1 1302 | -2.3277,1.4381,-0.82114,-1.2862,1 1303 | -3.7244,1.9037,-0.035421,-2.5095,1 1304 | -2.5724,-0.95602,2.7073,-0.16639,1 1305 | -3.9297,-6.0816,10.0958,-1.0147,1 1306 | -5.2943,-5.1463,10.3332,-1.1181,1 1307 | -3.8953,4.0392,-0.3019,-2.1836,1 1308 | -1.2244,1.7485,-1.4801,-1.4181,1 1309 | -2.6406,-4.4159,5.983,-0.13924,1 1310 | -4.6338,-12.7509,16.7166,-3.2168,1 1311 | -4.2887,-7.8633,11.8387,-1.8978,1 1312 | -3.3458,-0.50491,2.6328,0.53705,1 1313 | -1.1188,3.3357,-1.3455,-1.9573,1 1314 | 0.55939,-0.3104,0.18307,0.44653,1 1315 | -1.5078,-7.3191,7.8981,1.2289,1 1316 | -3.506,-12.5667,15.1606,-0.75216,1 1317 | -2.9498,-8.273,10.2646,1.1629,1 1318 | -1.6029,-0.38903,1.62,1.9103,1 1319 | -1.2667,2.8183,-2.426,-1.8862,1 1320 | -0.49281,3.0605,-1.8356,-2.834,1 1321 | 0.66365,-0.045533,-0.18794,0.23447,1 1322 | -0.72068,-6.7583,5.8408,0.62369,1 1323 | -1.9966,-9.5001,9.682,-0.12889,1 1324 | -0.97325,-6.4168,5.6026,1.0323,1 1325 | -0.025314,-0.17383,-0.11339,1.2198,1 1326 | 0.062525,2.9301,-3.5467,-2.6737,1 1327 | -5.525,6.3258,0.89768,-6.6241,1 1328 | -1.2943,2.6735,-0.84085,-2.0323,1 1329 | -0.24037,-1.7837,2.135,1.2418,1 1330 | -1.3968,-9.6698,9.4652,-0.34872,1 1331 | -2.9672,-13.2869,13.4727,-2.6271,1 1332 | -1.1005,-7.2508,6.0139,0.36895,1 1333 | 0.22432,-0.52147,-0.40386,1.2017,1 1334 | 0.90407,3.3708,-4.4987,-3.6965,1 1335 | -2.8619,4.5193,-0.58123,-4.2629,1 1336 | -1.0833,-0.31247,1.2815,0.41291,1 1337 | -1.5681,-7.2446,6.5537,-0.1276,1 1338 | -2.0545,-10.8679,9.4926,-1.4116,1 1339 | 0.2346,-4.5152,2.1195,1.4448,1 1340 | 1.581,0.86909,-2.3138,0.82412,1 1341 | 1.5514,3.8013,-4.9143,-3.7483,1 1342 | -4.1479,7.1225,-0.083404,-6.4172,1 1343 | -2.2625,-0.099335,2.8127,0.48662,1 1344 | -1.7479,-5.823,5.8699,1.212,1 1345 | -0.95923,-6.7128,4.9857,0.32886,1 1346 | 1.3451,0.23589,-1.8785,1.3258,1 1347 | 2.2279,4.0951,-4.8037,-2.1112,1 1348 | 1.2572,4.8731,-5.2861,-5.8741,1 1349 | -5.3857,9.1214,-0.41929,-5.9181,1 1350 | -2.9786,2.3445,0.52667,-0.40173,1 1351 | -1.5851,-2.1562,1.7082,0.9017,1 1352 | -0.21888,-2.2038,-0.0954,0.56421,1 1353 | 1.3183,1.9017,-3.3111,0.065071,1 1354 | 1.4896,3.4288,-4.0309,-1.4259,1 1355 | 0.11592,3.2219,-3.4302,-2.8457,1 1356 | -3.3924,3.3564,-0.72004,-3.5233,1 1357 | -6.1632,8.7096,-0.21621,-3.6345,1 1358 | -4.0786,2.9239,0.87026,-0.65389,1 1359 | -2.5899,-0.3911,0.93452,0.42972,1 1360 | -1.0116,-0.19038,-0.90597,0.003003,1 1361 | 0.066129,2.4914,-2.9401,-0.62156,1 1362 | -0.24745,1.9368,-2.4697,-0.80518,1 1363 | -1.5732,1.0636,-0.71232,-0.8388,1 1364 | -2.1668,1.5933,0.045122,-1.678,1 1365 | -1.1667,-1.4237,2.9241,0.66119,1 1366 | -2.8391,-6.63,10.4849,-0.42113,1 1367 | -4.5046,-5.8126,10.8867,-0.52846,1 1368 | -2.41,3.7433,-0.40215,-1.2953,1 1369 | 0.40614,1.3492,-1.4501,-0.55949,1 1370 | -1.3887,-4.8773,6.4774,0.34179,1 1371 | -3.7503,-13.4586,17.5932,-2.7771,1 1372 | -3.5637,-8.3827,12.393,-1.2823,1 1373 | -2.5419,-0.65804,2.6842,1.1952,1 -------------------------------------------------------------------------------- /tracking/data/petrol_consumption.csv: -------------------------------------------------------------------------------- 1 | Petrol_tax,Average_income,Paved_Highways,Population_Driver_licence(%),Petrol_Consumption 2 | 9.00,3571,1976,0.5250,541 3 | 9.00,4092,1250,0.5720,524 4 | 9.00,3865,1586,0.5800,561 5 | 7.50,4870,2351,0.5290,414 6 | 8.00,4399,431,0.5440,410 7 | 10.00,5342,1333,0.5710,457 8 | 8.00,5319,11868,0.4510,344 9 | 8.00,5126,2138,0.5530,467 10 | 8.00,4447,8577,0.5290,464 11 | 7.00,4512,8507,0.5520,498 12 | 8.00,4391,5939,0.5300,580 13 | 7.50,5126,14186,0.5250,471 14 | 7.00,4817,6930,0.5740,525 15 | 7.00,4207,6580,0.5450,508 16 | 7.00,4332,8159,0.6080,566 17 | 7.00,4318,10340,0.5860,635 18 | 7.00,4206,8508,0.5720,603 19 | 7.00,3718,4725,0.5400,714 20 | 7.00,4716,5915,0.7240,865 21 | 8.50,4341,6010,0.6770,640 22 | 7.00,4593,7834,0.6630,649 23 | 8.00,4983,602,0.6020,540 24 | 9.00,4897,2449,0.5110,464 25 | 9.00,4258,4686,0.5170,547 26 | 8.50,4574,2619,0.5510,460 27 | 9.00,3721,4746,0.5440,566 28 | 8.00,3448,5399,0.5480,577 29 | 7.50,3846,9061,0.5790,631 30 | 8.00,4188,5975,0.5630,574 31 | 9.00,3601,4650,0.4930,534 32 | 7.00,3640,6905,0.5180,571 33 | 7.00,3333,6594,0.5130,554 34 | 8.00,3063,6524,0.5780,577 35 | 7.50,3357,4121,0.5470,628 36 | 8.00,3528,3495,0.4870,487 37 | 6.58,3802,7834,0.6290,644 38 | 5.00,4045,17782,0.5660,640 39 | 7.00,3897,6385,0.5860,704 40 | 8.50,3635,3274,0.6630,648 41 | 7.00,4345,3905,0.6720,968 42 | 7.00,4449,4639,0.6260,587 43 | 7.00,3656,3985,0.5630,699 44 | 7.00,4300,3635,0.6030,632 45 | 7.00,3745,2611,0.5080,591 46 | 6.00,5215,2302,0.6720,782 47 | 9.00,4476,3942,0.5710,510 48 | 7.00,4296,4083,0.6230,610 49 | 7.00,5002,9794,0.5930,524 50 | -------------------------------------------------------------------------------- /tracking/data/test_bill_authentication.csv: -------------------------------------------------------------------------------- 1 | Variance,Skewness,Curtosis,Entropy,Class 2 | 3.6216,8.6661,-2.8073,-0.44699,0 3 | 4.5459,8.1674,-2.4586,-1.4621,0 4 | 3.866,-2.6383,1.9242,0.10645,0 5 | -------------------------------------------------------------------------------- /tracking/data/test_petrol_consumption.csv: -------------------------------------------------------------------------------- 1 | Petrol_tax,Average_income,Paved_Highways,Population_Driver_licence(%),Petrol_Consumption 2 | 9.00,4476,3942,0.5710,510 3 | 7.00,5002,9794,0.5930,524 4 | -------------------------------------------------------------------------------- /tracking/images/databricks_ce_create_mlr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/images/databricks_ce_create_mlr.png -------------------------------------------------------------------------------- /tracking/images/databricks_ce_download_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/images/databricks_ce_download_notebooks.png -------------------------------------------------------------------------------- /tracking/images/databricks_ce_import_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/images/databricks_ce_import_notebooks.png -------------------------------------------------------------------------------- /tracking/images/databricks_ce_loging.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/images/databricks_ce_loging.png -------------------------------------------------------------------------------- /tracking/images/mlflow-workshop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/images/mlflow-workshop.png -------------------------------------------------------------------------------- /tracking/images/mlflow-workshop1-youtube.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/images/mlflow-workshop1-youtube.png -------------------------------------------------------------------------------- /tracking/notebooks/dbc/MLflow-Tracking.dbc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/notebooks/dbc/MLflow-Tracking.dbc -------------------------------------------------------------------------------- /tracking/slides/mlflow-tracking-module.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/tmls-workshop/ff6a6e4a7fbd5227142816821bf8699289e6f405/tracking/slides/mlflow-tracking-module.pdf --------------------------------------------------------------------------------