├── .gitignore ├── LICENSE ├── README.md ├── gde_cnn_interpretation_xai ├── Interpreting_Vision_Models_1.ipynb └── Interpreting_Vision_Models_2.ipynb ├── gde_tf_serve_vision └── tensorflow_serving │ ├── Model_Training_Prototypes.ipynb │ ├── app.py │ ├── models.conf │ ├── serve_warmup_data.npy │ └── sneaker.jpg ├── misc └── deeppunct_params_en.zip ├── os_malaria_detection ├── Malaria Detection - Deep Learning Healthcare Case-Study.ipynb └── model_evaluation_utils.py ├── tds_deep_transfer_learning_nlp_classification ├── Deep Transfer Learning for NLP - Text Classification with Universal Embeddings.ipynb ├── movie_reviews.csv.bz2 ├── sample_classification.png └── sentiment_cover.png ├── tds_effective_data_visualization ├── Bonus - Effective Multi-dimensional Data Visualization.ipynb ├── winequality-red.csv ├── winequality-white.csv └── winequality.names ├── tds_feature_engineering_text_data ├── Feature Engineering Text Data - Advanced Deep Learning Strategies.ipynb └── Feature Engineering Text Data - Traditional Strategies.ipynb ├── tds_model_interpretation_xai ├── Human-interpretable Machine Learning - DS.ipynb ├── dtree_structure.svg └── model_evaluation_utils.py ├── tds_nlp_practitioners_guide ├── NLP Strategy I - Processing and Understanding Text.ipynb ├── Text Classification Demo.ipynb ├── contractions.py ├── model_evaluation_utils.py └── news.csv ├── tds_scalable_log_analytics └── Scalable_Log_Analytics_Spark.ipynb └── tds_spark_sql_intro ├── Working with SQL at Scale - Spark SQL Tutorial.ipynb ├── kdd99_dataset_details.pdf ├── kdd99_feature_details.pdf └── kdd99_feature_names.pdf /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # data_science_for_all 2 | Code and resources for my blog and articles to share Data Science and AI knowledge and learnings with everyone 3 | -------------------------------------------------------------------------------- /gde_tf_serve_vision/tensorflow_serving/app.py: -------------------------------------------------------------------------------- 1 | # load dependencies 2 | import warnings 3 | warnings.simplefilter(action='ignore', category=FutureWarning) 4 | import numpy as np 5 | import requests 6 | import base64 7 | import json 8 | from io import BytesIO 9 | from flask import Flask, request, jsonify 10 | from flask_cors import CORS 11 | import tensorflow as tf 12 | from tensorflow import keras 13 | import cv2 14 | 15 | # TF Serving Assets 16 | HEADERS = {'content-type': 'application/json'} 17 | MODEL2_API_URL = 'http://localhost:8501/v1/models/fashion_model_serving/versions/2:predict' 18 | CLASS_NAMES = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 19 | 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] 20 | 21 | # Instantiate Flask App 22 | app = Flask(__name__) 23 | CORS(app) 24 | 25 | # Image resizing utils 26 | def resize_image_array(img, img_size_dims): 27 | img = cv2.resize(img, dsize=img_size_dims, 28 | interpolation=cv2.INTER_CUBIC) 29 | img = np.array(img, dtype=np.float32) 30 | return img 31 | 32 | # Model warmup function 33 | def warmup_model2_serve(warmup_data, img_dims=(32, 32)): 34 | warmup_data_processed = (np.array([resize_image_array(img, 35 | img_size_dims=img_dims) 36 | for img in np.stack([warmup_data]*3, 37 | axis=-1)])) / 255. 38 | data = json.dumps({"signature_name": "serving_default", 39 | "instances": warmup_data_processed.tolist()}) 40 | 41 | json_response = requests.post(MODEL2_API_URL, data=data, headers=HEADERS) 42 | predictions = json.loads(json_response.text)['predictions'] 43 | print('Model 2 warmup complete') # log this in actual production 44 | 45 | # TF lazy loads so we warmup model with sample data 46 | # This runs as soon as we setup our web service to run 47 | warmup_data = np.load('serve_warmup_data.npy') 48 | warmup_model2_serve(warmup_data) 49 | 50 | # Liveness test 51 | @app.route('/apparel_classifier/api/v1/liveness', methods=['GET', 'POST']) 52 | def liveness(): 53 | return 'API Live!' 54 | 55 | # Model 2 inference endpoint 56 | @app.route('/apparel_classifier/api/v1/model2_predict', methods=['POST']) 57 | def image_classifier_model2(): 58 | img = np.array([keras.preprocessing.image.img_to_array( 59 | keras.preprocessing.image.load_img(BytesIO(base64.b64decode(request.form['b64_img'])), 60 | target_size=(32, 32))) / 255.]) 61 | 62 | data = json.dumps({"signature_name": "serving_default", 63 | "instances": img.tolist()}) 64 | 65 | json_response = requests.post(MODEL2_API_URL, data=data, headers=HEADERS) 66 | prediction = json.loads(json_response.text)['predictions'] 67 | prediction = np.argmax(np.array(prediction), axis=1)[0] 68 | prediction = CLASS_NAMES[prediction] 69 | 70 | return jsonify({'apparel_type': prediction}) 71 | 72 | 73 | # running REST interface, port=5000 for direct test 74 | # use debug=True when debugging 75 | if __name__ == "__main__": 76 | app.run(debug=False, host='0.0.0.0', port=5000) -------------------------------------------------------------------------------- /gde_tf_serve_vision/tensorflow_serving/models.conf: -------------------------------------------------------------------------------- 1 | model_config_list: { 2 | config: { 3 | name: "fashion_model_serving", 4 | base_path: "/home/jupyter/tensorflow_serving/tf_saved_models", 5 | model_platform: "tensorflow", 6 | model_version_policy { 7 | specific { 8 | versions: 1 9 | versions: 2 10 | } 11 | } 12 | } 13 | } 14 | 15 | 16 | -------------------------------------------------------------------------------- /gde_tf_serve_vision/tensorflow_serving/serve_warmup_data.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dipanjanS/data_science_for_all/b1140caaa929526bf0c740d98a660c8f324d4899/gde_tf_serve_vision/tensorflow_serving/serve_warmup_data.npy -------------------------------------------------------------------------------- /gde_tf_serve_vision/tensorflow_serving/sneaker.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dipanjanS/data_science_for_all/b1140caaa929526bf0c740d98a660c8f324d4899/gde_tf_serve_vision/tensorflow_serving/sneaker.jpg -------------------------------------------------------------------------------- /misc/deeppunct_params_en.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dipanjanS/data_science_for_all/b1140caaa929526bf0c740d98a660c8f324d4899/misc/deeppunct_params_en.zip -------------------------------------------------------------------------------- /os_malaria_detection/model_evaluation_utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jul 31 20:05:23 2017 4 | 5 | @author: DIP 6 | @Copyright: Dipanjan Sarkar 7 | """ 8 | 9 | from sklearn import metrics 10 | import numpy as np 11 | import pandas as pd 12 | import matplotlib.pyplot as plt 13 | from sklearn.preprocessing import LabelEncoder 14 | from sklearn.base import clone 15 | from sklearn.preprocessing import label_binarize 16 | from scipy import interp 17 | from sklearn.metrics import roc_curve, auc 18 | 19 | 20 | 21 | def get_metrics(true_labels, predicted_labels): 22 | 23 | return { 24 | 'Accuracy': np.round( 25 | metrics.accuracy_score(true_labels, 26 | predicted_labels), 27 | 4), 28 | 'Precision:': np.round( 29 | metrics.precision_score(true_labels, 30 | predicted_labels, 31 | average='weighted'), 32 | 4), 33 | 'Recall': np.round( 34 | metrics.recall_score(true_labels, 35 | predicted_labels, 36 | average='weighted'), 37 | 4), 38 | 'F1 Score:': np.round( 39 | metrics.f1_score(true_labels, 40 | predicted_labels, 41 | average='weighted'), 42 | 4) 43 | } 44 | 45 | 46 | def display_metrics(true_labels, predicted_labels): 47 | 48 | print('Accuracy:', np.round( 49 | metrics.accuracy_score(true_labels, 50 | predicted_labels), 51 | 4)) 52 | print('Precision:', np.round( 53 | metrics.precision_score(true_labels, 54 | predicted_labels, 55 | average='weighted'), 56 | 4)) 57 | print('Recall:', np.round( 58 | metrics.recall_score(true_labels, 59 | predicted_labels, 60 | average='weighted'), 61 | 4)) 62 | print('F1 Score:', np.round( 63 | metrics.f1_score(true_labels, 64 | predicted_labels, 65 | average='weighted'), 66 | 4)) 67 | 68 | 69 | def train_predict_model(classifier, 70 | train_features, train_labels, 71 | test_features, test_labels): 72 | # build model 73 | classifier.fit(train_features, train_labels) 74 | # predict using model 75 | predictions = classifier.predict(test_features) 76 | return predictions 77 | 78 | 79 | def display_confusion_matrix(true_labels, predicted_labels, classes=[1,0]): 80 | 81 | total_classes = len(classes) 82 | level_labels = [total_classes*[0], list(range(total_classes))] 83 | 84 | cm = metrics.confusion_matrix(y_true=true_labels, y_pred=predicted_labels, 85 | labels=classes) 86 | cm_frame = pd.DataFrame(data=cm, 87 | columns=pd.MultiIndex(levels=[['Predicted:'], classes], 88 | codes=level_labels), 89 | index=pd.MultiIndex(levels=[['Actual:'], classes], 90 | codes=level_labels)) 91 | print(cm_frame) 92 | 93 | 94 | def display_confusion_matrix_pretty(true_labels, predicted_labels, classes=[1,0]): 95 | 96 | total_classes = len(classes) 97 | level_labels = [total_classes*[0], list(range(total_classes))] 98 | 99 | cm = metrics.confusion_matrix(y_true=true_labels, y_pred=predicted_labels, 100 | labels=classes) 101 | cm_frame = pd.DataFrame(data=cm, 102 | columns=pd.MultiIndex(levels=[['Predicted:'], classes], 103 | codes=level_labels), 104 | index=pd.MultiIndex(levels=[['Actual:'], classes], 105 | codes=level_labels)) 106 | return cm_frame 107 | 108 | def display_classification_report(true_labels, predicted_labels, classes=[1,0]): 109 | 110 | report = metrics.classification_report(y_true=true_labels, 111 | y_pred=predicted_labels, 112 | labels=classes) 113 | print(report) 114 | 115 | 116 | 117 | def display_model_performance_metrics(true_labels, predicted_labels, classes=[1,0]): 118 | print('Model Performance metrics:') 119 | print('-'*30) 120 | get_metrics(true_labels=true_labels, predicted_labels=predicted_labels) 121 | print('\nModel Classification report:') 122 | print('-'*30) 123 | display_classification_report(true_labels=true_labels, predicted_labels=predicted_labels, 124 | classes=classes) 125 | print('\nPrediction Confusion Matrix:') 126 | print('-'*30) 127 | display_confusion_matrix(true_labels=true_labels, predicted_labels=predicted_labels, 128 | classes=classes) 129 | 130 | 131 | def plot_model_decision_surface(clf, train_features, train_labels, 132 | plot_step=0.02, cmap=plt.cm.RdYlBu, 133 | markers=None, alphas=None, colors=None): 134 | 135 | if train_features.shape[1] != 2: 136 | raise ValueError("X_train should have exactly 2 columnns!") 137 | 138 | x_min, x_max = train_features[:, 0].min() - plot_step, train_features[:, 0].max() + plot_step 139 | y_min, y_max = train_features[:, 1].min() - plot_step, train_features[:, 1].max() + plot_step 140 | xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), 141 | np.arange(y_min, y_max, plot_step)) 142 | 143 | clf_est = clone(clf) 144 | clf_est.fit(train_features,train_labels) 145 | if hasattr(clf_est, 'predict_proba'): 146 | Z = clf_est.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1] 147 | else: 148 | Z = clf_est.predict(np.c_[xx.ravel(), yy.ravel()]) 149 | Z = Z.reshape(xx.shape) 150 | cs = plt.contourf(xx, yy, Z, cmap=cmap) 151 | 152 | le = LabelEncoder() 153 | y_enc = le.fit_transform(train_labels) 154 | n_classes = len(le.classes_) 155 | plot_colors = ''.join(colors) if colors else [None] * n_classes 156 | label_names = le.classes_ 157 | markers = markers if markers else [None] * n_classes 158 | alphas = alphas if alphas else [None] * n_classes 159 | for i, color in zip(range(n_classes), plot_colors): 160 | idx = np.where(y_enc == i) 161 | plt.scatter(train_features[idx, 0], train_features[idx, 1], c=color, 162 | label=label_names[i], cmap=cmap, edgecolors='black', 163 | marker=markers[i], alpha=alphas[i]) 164 | plt.legend() 165 | plt.show() 166 | 167 | 168 | def plot_model_roc_curve(clf, features, true_labels, label_encoder=None, class_names=None): 169 | 170 | ## Compute ROC curve and ROC area for each class 171 | fpr = dict() 172 | tpr = dict() 173 | roc_auc = dict() 174 | if hasattr(clf, 'classes_'): 175 | class_labels = clf.classes_ 176 | elif label_encoder: 177 | class_labels = label_encoder.classes_ 178 | elif class_names: 179 | class_labels = class_names 180 | else: 181 | raise ValueError('Unable to derive prediction classes, please specify class_names!') 182 | n_classes = len(class_labels) 183 | y_test = label_binarize(true_labels, classes=class_labels) 184 | if n_classes == 2: 185 | if hasattr(clf, 'predict_proba'): 186 | prob = clf.predict_proba(features) 187 | y_score = prob[:, prob.shape[1]-1] 188 | elif hasattr(clf, 'decision_function'): 189 | prob = clf.decision_function(features) 190 | y_score = prob[:, prob.shape[1]-1] 191 | else: 192 | raise AttributeError("Estimator doesn't have a probability or confidence scoring system!") 193 | 194 | fpr, tpr, _ = roc_curve(y_test, y_score) 195 | roc_auc = auc(fpr, tpr) 196 | plt.plot(fpr, tpr, label='ROC curve (area = {0:0.2f})' 197 | ''.format(roc_auc), 198 | linewidth=2.5) 199 | 200 | elif n_classes > 2: 201 | if hasattr(clf, 'predict_proba'): 202 | y_score = clf.predict_proba(features) 203 | elif hasattr(clf, 'decision_function'): 204 | y_score = clf.decision_function(features) 205 | else: 206 | raise AttributeError("Estimator doesn't have a probability or confidence scoring system!") 207 | 208 | for i in range(n_classes): 209 | fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) 210 | roc_auc[i] = auc(fpr[i], tpr[i]) 211 | 212 | ## Compute micro-average ROC curve and ROC area 213 | fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) 214 | roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) 215 | 216 | ## Compute macro-average ROC curve and ROC area 217 | # First aggregate all false positive rates 218 | all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) 219 | # Then interpolate all ROC curves at this points 220 | mean_tpr = np.zeros_like(all_fpr) 221 | for i in range(n_classes): 222 | mean_tpr += interp(all_fpr, fpr[i], tpr[i]) 223 | # Finally average it and compute AUC 224 | mean_tpr /= n_classes 225 | fpr["macro"] = all_fpr 226 | tpr["macro"] = mean_tpr 227 | roc_auc["macro"] = auc(fpr["macro"], tpr["macro"]) 228 | 229 | ## Plot ROC curves 230 | plt.figure(figsize=(6, 4)) 231 | plt.plot(fpr["micro"], tpr["micro"], 232 | label='micro-average ROC curve (area = {0:0.2f})' 233 | ''.format(roc_auc["micro"]), linewidth=3) 234 | 235 | plt.plot(fpr["macro"], tpr["macro"], 236 | label='macro-average ROC curve (area = {0:0.2f})' 237 | ''.format(roc_auc["macro"]), linewidth=3) 238 | 239 | for i, label in enumerate(class_labels): 240 | plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})' 241 | ''.format(label, roc_auc[i]), 242 | linewidth=2, linestyle=':') 243 | else: 244 | raise ValueError('Number of classes should be atleast 2 or more') 245 | 246 | plt.plot([0, 1], [0, 1], 'k--') 247 | plt.xlim([0.0, 1.0]) 248 | plt.ylim([0.0, 1.05]) 249 | plt.xlabel('False Positive Rate') 250 | plt.ylabel('True Positive Rate') 251 | plt.title('Receiver Operating Characteristic (ROC) Curve') 252 | plt.legend(loc="lower right") 253 | plt.show() 254 | 255 | 256 | -------------------------------------------------------------------------------- /tds_deep_transfer_learning_nlp_classification/movie_reviews.csv.bz2: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dipanjanS/data_science_for_all/b1140caaa929526bf0c740d98a660c8f324d4899/tds_deep_transfer_learning_nlp_classification/movie_reviews.csv.bz2 -------------------------------------------------------------------------------- /tds_deep_transfer_learning_nlp_classification/sample_classification.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dipanjanS/data_science_for_all/b1140caaa929526bf0c740d98a660c8f324d4899/tds_deep_transfer_learning_nlp_classification/sample_classification.png -------------------------------------------------------------------------------- /tds_deep_transfer_learning_nlp_classification/sentiment_cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dipanjanS/data_science_for_all/b1140caaa929526bf0c740d98a660c8f324d4899/tds_deep_transfer_learning_nlp_classification/sentiment_cover.png -------------------------------------------------------------------------------- /tds_effective_data_visualization/winequality.names: -------------------------------------------------------------------------------- 1 | Citation Request: 2 | This dataset is public available for research. The details are described in [Cortez et al., 2009]. 3 | Please include this citation if you plan to use this database: 4 | 5 | P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 6 | Modeling wine preferences by data mining from physicochemical properties. 7 | In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236. 8 | 9 | Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 10 | [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf 11 | [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib 12 | 13 | 1. Title: Wine Quality 14 | 15 | 2. Sources 16 | Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009 17 | 18 | 3. Past Usage: 19 | 20 | P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 21 | Modeling wine preferences by data mining from physicochemical properties. 22 | In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236. 23 | 24 | In the above reference, two datasets were created, using red and white wine samples. 25 | The inputs include objective tests (e.g. PH values) and the output is based on sensory data 26 | (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality 27 | between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model 28 | these datasets under a regression approach. The support vector machine model achieved the 29 | best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), 30 | etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity 31 | analysis procedure). 32 | 33 | 4. Relevant Information: 34 | 35 | The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. 36 | For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. 37 | Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables 38 | are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). 39 | 40 | These datasets can be viewed as classification or regression tasks. 41 | The classes are ordered and not balanced (e.g. there are munch more normal wines than 42 | excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent 43 | or poor wines. Also, we are not sure if all input variables are relevant. So 44 | it could be interesting to test feature selection methods. 45 | 46 | 5. Number of Instances: red wine - 1599; white wine - 4898. 47 | 48 | 6. Number of Attributes: 11 + output attribute 49 | 50 | Note: several of the attributes may be correlated, thus it makes sense to apply some sort of 51 | feature selection. 52 | 53 | 7. Attribute information: 54 | 55 | For more information, read [Cortez et al., 2009]. 56 | 57 | Input variables (based on physicochemical tests): 58 | 1 - fixed acidity 59 | 2 - volatile acidity 60 | 3 - citric acid 61 | 4 - residual sugar 62 | 5 - chlorides 63 | 6 - free sulfur dioxide 64 | 7 - total sulfur dioxide 65 | 8 - density 66 | 9 - pH 67 | 10 - sulphates 68 | 11 - alcohol 69 | Output variable (based on sensory data): 70 | 12 - quality (score between 0 and 10) 71 | 72 | 8. Missing Attribute Values: None 73 | -------------------------------------------------------------------------------- /tds_model_interpretation_xai/dtree_structure.svg: -------------------------------------------------------------------------------- 1 | 2 | 4 | 6 | 7 | 9 | 10 | Tree 11 | 12 | 13 | 0 14 | 15 | node #0 16 | X 17 | 5 18 | ≤ 0.5 19 | gini = 0.5 20 | samples = 100.0% 21 | value = [0.5, 0.5] 22 | class = More than $50K 23 | 24 | 25 | 1 26 | 27 | node #1 28 | X 29 | 2 30 | ≤ 9.5 31 | gini = 0.4 32 | samples = 40.7% 33 | value = [0.277, 0.723] 34 | class = More than $50K 35 | 36 | 37 | 0->1 38 | 39 | 40 | True 41 | 42 | 43 | 2 44 | 45 | node #2 46 | X 47 | 5 48 | ≤ 4.5 49 | gini = 0.354 50 | samples = 59.3% 51 | value = [0.77, 0.23] 52 | class = $50K or less 53 | 54 | 55 | 0->2 56 | 57 | 58 | False 59 | 60 | 61 | 5 62 | 63 | node #5 64 | X 65 | 8 66 | ≤ 4225.0 67 | gini = 0.466 68 | samples = 17.6% 69 | value = [0.631, 0.369] 70 | class = $50K or less 71 | 72 | 73 | 1->5 74 | 75 | 76 | 77 | 78 | 6 79 | 80 | node #6 81 | X 82 | 2 83 | ≤ 12.5 84 | gini = 0.241 85 | samples = 23.2% 86 | value = [0.14, 0.86] 87 | class = More than $50K 88 | 89 | 90 | 1->6 91 | 92 | 93 | 94 | 95 | 9 96 | 97 | node #9 98 | gini = 0.416 99 | samples = 16.7% 100 | value = [0.705, 0.295] 101 | class = $50K or less 102 | 103 | 104 | 5->9 105 | 106 | 107 | 108 | 109 | 10 110 | 111 | node #10 112 | gini = 0.103 113 | samples = 0.8% 114 | value = [0.055, 0.945] 115 | class = More than $50K 116 | 117 | 118 | 5->10 119 | 120 | 121 | 122 | 123 | 11 124 | 125 | node #11 126 | X 127 | 0 128 | ≤ 35.5 129 | gini = 0.419 130 | samples = 10.8% 131 | value = [0.299, 0.701] 132 | class = More than $50K 133 | 134 | 135 | 6->11 136 | 137 | 138 | 139 | 140 | 12 141 | 142 | node #12 143 | gini = 0.1 144 | samples = 12.4% 145 | value = [0.053, 0.947] 146 | class = More than $50K 147 | 148 | 149 | 6->12 150 | 151 | 152 | 153 | 154 | 13 155 | 156 | node #13 157 | gini = 0.437 158 | samples = 3.3% 159 | value = [0.678, 0.322] 160 | class = $50K or less 161 | 162 | 163 | 11->13 164 | 165 | 166 | 167 | 168 | 14 169 | 170 | node #14 171 | gini = 0.328 172 | samples = 7.5% 173 | value = [0.207, 0.793] 174 | class = More than $50K 175 | 176 | 177 | 11->14 178 | 179 | 180 | 181 | 182 | 3 183 | 184 | node #3 185 | X 186 | 8 187 | ≤ 4668.5 188 | gini = 0.215 189 | samples = 54.5% 190 | value = [0.878, 0.122] 191 | class = $50K or less 192 | 193 | 194 | 2->3 195 | 196 | 197 | 198 | 199 | 4 200 | 201 | node #4 202 | gini = 0.325 203 | samples = 4.7% 204 | value = [0.205, 0.795] 205 | class = More than $50K 206 | 207 | 208 | 2->4 209 | 210 | 211 | 212 | 213 | 7 214 | 215 | node #7 216 | gini = 0.117 217 | samples = 53.2% 218 | value = [0.938, 0.062] 219 | class = $50K or less 220 | 221 | 222 | 3->7 223 | 224 | 225 | 226 | 227 | 8 228 | 229 | node #8 230 | gini = 0.113 231 | samples = 1.3% 232 | value = [0.06, 0.94] 233 | class = More than $50K 234 | 235 | 236 | 3->8 237 | 238 | 239 | 240 | 241 | 242 | -------------------------------------------------------------------------------- /tds_model_interpretation_xai/model_evaluation_utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jul 31 20:05:23 2017 4 | 5 | @author: DIP 6 | """ 7 | 8 | from sklearn import metrics 9 | import numpy as np 10 | import pandas as pd 11 | import matplotlib.pyplot as plt 12 | from sklearn.preprocessing import LabelEncoder 13 | from sklearn.base import clone 14 | from sklearn.preprocessing import label_binarize 15 | from scipy import interp 16 | from sklearn.metrics import roc_curve, auc 17 | 18 | 19 | def get_metrics(true_labels, predicted_labels): 20 | 21 | print('Accuracy:', np.round( 22 | metrics.accuracy_score(true_labels, 23 | predicted_labels), 24 | 4)) 25 | print('Precision:', np.round( 26 | metrics.precision_score(true_labels, 27 | predicted_labels, 28 | average='weighted'), 29 | 4)) 30 | print('Recall:', np.round( 31 | metrics.recall_score(true_labels, 32 | predicted_labels, 33 | average='weighted'), 34 | 4)) 35 | print('F1 Score:', np.round( 36 | metrics.f1_score(true_labels, 37 | predicted_labels, 38 | average='weighted'), 39 | 4)) 40 | 41 | 42 | def train_predict_model(classifier, 43 | train_features, train_labels, 44 | test_features, test_labels): 45 | # build model 46 | classifier.fit(train_features, train_labels) 47 | # predict using model 48 | predictions = classifier.predict(test_features) 49 | return predictions 50 | 51 | 52 | def display_confusion_matrix(true_labels, predicted_labels, classes=[1,0]): 53 | 54 | total_classes = len(classes) 55 | level_labels = [total_classes*[0], list(range(total_classes))] 56 | 57 | cm = metrics.confusion_matrix(y_true=true_labels, y_pred=predicted_labels, 58 | labels=classes) 59 | cm_frame = pd.DataFrame(data=cm, 60 | columns=pd.MultiIndex(levels=[['Predicted:'], classes], 61 | labels=level_labels), 62 | index=pd.MultiIndex(levels=[['Actual:'], classes], 63 | labels=level_labels)) 64 | print(cm_frame) 65 | 66 | def display_classification_report(true_labels, predicted_labels, classes=[1,0]): 67 | 68 | report = metrics.classification_report(y_true=true_labels, 69 | y_pred=predicted_labels, 70 | labels=classes) 71 | print(report) 72 | 73 | 74 | 75 | def display_model_performance_metrics(true_labels, predicted_labels, classes=[1,0]): 76 | print('Model Performance metrics:') 77 | print('-'*30) 78 | get_metrics(true_labels=true_labels, predicted_labels=predicted_labels) 79 | print('\nModel Classification report:') 80 | print('-'*30) 81 | display_classification_report(true_labels=true_labels, predicted_labels=predicted_labels, 82 | classes=classes) 83 | print('\nPrediction Confusion Matrix:') 84 | print('-'*30) 85 | display_confusion_matrix(true_labels=true_labels, predicted_labels=predicted_labels, 86 | classes=classes) 87 | 88 | 89 | def plot_model_decision_surface(clf, train_features, train_labels, 90 | plot_step=0.02, cmap=plt.cm.RdYlBu, 91 | markers=None, alphas=None, colors=None): 92 | 93 | if train_features.shape[1] != 2: 94 | raise ValueError("X_train should have exactly 2 columnns!") 95 | 96 | x_min, x_max = train_features[:, 0].min() - plot_step, train_features[:, 0].max() + plot_step 97 | y_min, y_max = train_features[:, 1].min() - plot_step, train_features[:, 1].max() + plot_step 98 | xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), 99 | np.arange(y_min, y_max, plot_step)) 100 | 101 | clf_est = clone(clf) 102 | clf_est.fit(train_features,train_labels) 103 | if hasattr(clf_est, 'predict_proba'): 104 | Z = clf_est.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1] 105 | else: 106 | Z = clf_est.predict(np.c_[xx.ravel(), yy.ravel()]) 107 | Z = Z.reshape(xx.shape) 108 | cs = plt.contourf(xx, yy, Z, cmap=cmap) 109 | 110 | le = LabelEncoder() 111 | y_enc = le.fit_transform(train_labels) 112 | n_classes = len(le.classes_) 113 | plot_colors = ''.join(colors) if colors else [None] * n_classes 114 | label_names = le.classes_ 115 | markers = markers if markers else [None] * n_classes 116 | alphas = alphas if alphas else [None] * n_classes 117 | for i, color in zip(range(n_classes), plot_colors): 118 | idx = np.where(y_enc == i) 119 | plt.scatter(train_features[idx, 0], train_features[idx, 1], c=color, 120 | label=label_names[i], cmap=cmap, edgecolors='black', 121 | marker=markers[i], alpha=alphas[i]) 122 | plt.legend() 123 | plt.show() 124 | 125 | 126 | def plot_model_roc_curve(clf, features, true_labels, label_encoder=None, class_names=None): 127 | 128 | ## Compute ROC curve and ROC area for each class 129 | fpr = dict() 130 | tpr = dict() 131 | roc_auc = dict() 132 | if hasattr(clf, 'classes_'): 133 | class_labels = clf.classes_ 134 | elif label_encoder: 135 | class_labels = label_encoder.classes_ 136 | elif class_names: 137 | class_labels = class_names 138 | else: 139 | raise ValueError('Unable to derive prediction classes, please specify class_names!') 140 | n_classes = len(class_labels) 141 | y_test = label_binarize(true_labels, classes=class_labels) 142 | if n_classes == 2: 143 | if hasattr(clf, 'predict_proba'): 144 | prob = clf.predict_proba(features) 145 | y_score = prob[:, prob.shape[1]-1] 146 | elif hasattr(clf, 'decision_function'): 147 | prob = clf.decision_function(features) 148 | y_score = prob[:, prob.shape[1]-1] 149 | else: 150 | raise AttributeError("Estimator doesn't have a probability or confidence scoring system!") 151 | 152 | fpr, tpr, _ = roc_curve(y_test, y_score) 153 | roc_auc = auc(fpr, tpr) 154 | plt.plot(fpr, tpr, label='ROC curve (area = {0:0.2f})' 155 | ''.format(roc_auc), 156 | linewidth=2.5) 157 | 158 | elif n_classes > 2: 159 | if hasattr(clf, 'predict_proba'): 160 | y_score = clf.predict_proba(features) 161 | elif hasattr(clf, 'decision_function'): 162 | y_score = clf.decision_function(features) 163 | else: 164 | raise AttributeError("Estimator doesn't have a probability or confidence scoring system!") 165 | 166 | for i in range(n_classes): 167 | fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) 168 | roc_auc[i] = auc(fpr[i], tpr[i]) 169 | 170 | ## Compute micro-average ROC curve and ROC area 171 | fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) 172 | roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) 173 | 174 | ## Compute macro-average ROC curve and ROC area 175 | # First aggregate all false positive rates 176 | all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) 177 | # Then interpolate all ROC curves at this points 178 | mean_tpr = np.zeros_like(all_fpr) 179 | for i in range(n_classes): 180 | mean_tpr += interp(all_fpr, fpr[i], tpr[i]) 181 | # Finally average it and compute AUC 182 | mean_tpr /= n_classes 183 | fpr["macro"] = all_fpr 184 | tpr["macro"] = mean_tpr 185 | roc_auc["macro"] = auc(fpr["macro"], tpr["macro"]) 186 | 187 | ## Plot ROC curves 188 | plt.figure(figsize=(6, 4)) 189 | plt.plot(fpr["micro"], tpr["micro"], 190 | label='micro-average ROC curve (area = {0:0.2f})' 191 | ''.format(roc_auc["micro"]), linewidth=3) 192 | 193 | plt.plot(fpr["macro"], tpr["macro"], 194 | label='macro-average ROC curve (area = {0:0.2f})' 195 | ''.format(roc_auc["macro"]), linewidth=3) 196 | 197 | for i, label in enumerate(class_labels): 198 | plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})' 199 | ''.format(label, roc_auc[i]), 200 | linewidth=2, linestyle=':') 201 | else: 202 | raise ValueError('Number of classes should be atleast 2 or more') 203 | 204 | plt.plot([0, 1], [0, 1], 'k--') 205 | plt.xlim([0.0, 1.0]) 206 | plt.ylim([0.0, 1.05]) 207 | plt.xlabel('False Positive Rate') 208 | plt.ylabel('True Positive Rate') 209 | plt.title('Receiver Operating Characteristic (ROC) Curve') 210 | plt.legend(loc="lower right") 211 | plt.show() 212 | 213 | 214 | -------------------------------------------------------------------------------- /tds_nlp_practitioners_guide/Text Classification Demo.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 21, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "\n", 13 | "pd.options.display.max_colwidth = 100\n", 14 | "%matplotlib inline" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 22, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "data = pd.read_csv('news.csv')" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 23, 29 | "metadata": {}, 30 | "outputs": [ 31 | { 32 | "data": { 33 | "text/html": [ 34 | "
\n", 35 | "\n", 48 | "\n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | "
news_headlinenews_articlenews_categoryfull_textclean_text
0World's cheapest phone 'Freedom 251' maker's founder arrestedThe maker of world's cheapest smartphone 'Freedom 251' priced at ₹251, Ringing Bells' founder Mo...technologyWorld's cheapest phone 'Freedom 251' maker's founder arrested. The maker of world's cheapest sma...world cheapest phone freedom maker founder arrest maker world cheapest smartphone freedom price ...
1US unveils world's most powerful supercomputer, beats ChinaThe US has unveiled the world's most powerful supercomputer called 'Summit', beating the previou...technologyUS unveils world's most powerful supercomputer, beats China. The US has unveiled the world's mos...us unveils world powerful supercomputer beat china us unveil world powerful supercomputer call s...
2FB bug changed 1.4 cr users’ privacy setting to ‘public’Facebook has said it recently found a bug that automatically suggested posting publicly when peo...technologyFB bug changed 1.4 cr users’ privacy setting to ‘public’. Facebook has said it recently found a ...fb bug change cr user privacy set public facebook say recently find bug automatically suggest po...
3Contest for 1st couple to marry in self-driving car launchedThe American Automobile Association has launched a contest to find the first couple to get marri...technologyContest for 1st couple to marry in self-driving car launched. The American Automobile Associatio...cont st couple marry self drive car launch american automobile association launch contest find f...
4China's ZTE to pay $1 billion fine to US to lift sanctionsChinese telecommunications equipment maker ZTE has agreed to a $1 billion fine to end US sanctio...technologyChina's ZTE to pay $1 billion fine to US to lift sanctions. Chinese telecommunications equipment...china zte pay billion fine us lift sanction chinese telecommunications equipment maker zte agree...
\n", 102 | "
" 103 | ], 104 | "text/plain": [ 105 | " news_headline \\\n", 106 | "0 World's cheapest phone 'Freedom 251' maker's founder arrested \n", 107 | "1 US unveils world's most powerful supercomputer, beats China \n", 108 | "2 FB bug changed 1.4 cr users’ privacy setting to ‘public’ \n", 109 | "3 Contest for 1st couple to marry in self-driving car launched \n", 110 | "4 China's ZTE to pay $1 billion fine to US to lift sanctions \n", 111 | "\n", 112 | " news_article \\\n", 113 | "0 The maker of world's cheapest smartphone 'Freedom 251' priced at ₹251, Ringing Bells' founder Mo... \n", 114 | "1 The US has unveiled the world's most powerful supercomputer called 'Summit', beating the previou... \n", 115 | "2 Facebook has said it recently found a bug that automatically suggested posting publicly when peo... \n", 116 | "3 The American Automobile Association has launched a contest to find the first couple to get marri... \n", 117 | "4 Chinese telecommunications equipment maker ZTE has agreed to a $1 billion fine to end US sanctio... \n", 118 | "\n", 119 | " news_category \\\n", 120 | "0 technology \n", 121 | "1 technology \n", 122 | "2 technology \n", 123 | "3 technology \n", 124 | "4 technology \n", 125 | "\n", 126 | " full_text \\\n", 127 | "0 World's cheapest phone 'Freedom 251' maker's founder arrested. The maker of world's cheapest sma... \n", 128 | "1 US unveils world's most powerful supercomputer, beats China. The US has unveiled the world's mos... \n", 129 | "2 FB bug changed 1.4 cr users’ privacy setting to ‘public’. Facebook has said it recently found a ... \n", 130 | "3 Contest for 1st couple to marry in self-driving car launched. The American Automobile Associatio... \n", 131 | "4 China's ZTE to pay $1 billion fine to US to lift sanctions. Chinese telecommunications equipment... \n", 132 | "\n", 133 | " clean_text \n", 134 | "0 world cheapest phone freedom maker founder arrest maker world cheapest smartphone freedom price ... \n", 135 | "1 us unveils world powerful supercomputer beat china us unveil world powerful supercomputer call s... \n", 136 | "2 fb bug change cr user privacy set public facebook say recently find bug automatically suggest po... \n", 137 | "3 cont st couple marry self drive car launch american automobile association launch contest find f... \n", 138 | "4 china zte pay billion fine us lift sanction chinese telecommunications equipment maker zte agree... " 139 | ] 140 | }, 141 | "execution_count": 23, 142 | "metadata": {}, 143 | "output_type": "execute_result" 144 | } 145 | ], 146 | "source": [ 147 | "data.head()" 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": 24, 153 | "metadata": {}, 154 | "outputs": [ 155 | { 156 | "data": { 157 | "text/plain": [ 158 | "\"US unveils world's most powerful supercomputer, beats China\"" 159 | ] 160 | }, 161 | "execution_count": 24, 162 | "metadata": {}, 163 | "output_type": "execute_result" 164 | } 165 | ], 166 | "source": [ 167 | "data.iloc[1]['news_headline']" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 25, 173 | "metadata": {}, 174 | "outputs": [ 175 | { 176 | "data": { 177 | "text/plain": [ 178 | "\"The US has unveiled the world's most powerful supercomputer called 'Summit', beating the previous record-holder China's Sunway TaihuLight. With a peak performance of 200,000 trillion calculations per second, it is over twice as fast as Sunway TaihuLight, which is capable of 93,000 trillion calculations per second. Summit has 4,608 servers, which reportedly take up the size of two tennis courts.\"" 179 | ] 180 | }, 181 | "execution_count": 25, 182 | "metadata": {}, 183 | "output_type": "execute_result" 184 | } 185 | ], 186 | "source": [ 187 | "data.iloc[1]['news_article']" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 26, 193 | "metadata": {}, 194 | "outputs": [ 195 | { 196 | "data": { 197 | "text/plain": [ 198 | "\"US unveils world's most powerful supercomputer, beats China. The US has unveiled the world's most powerful supercomputer called 'Summit', beating the previous record-holder China's Sunway TaihuLight. With a peak performance of 200,000 trillion calculations per second, it is over twice as fast as Sunway TaihuLight, which is capable of 93,000 trillion calculations per second. Summit has 4,608 servers, which reportedly take up the size of two tennis courts.\"" 199 | ] 200 | }, 201 | "execution_count": 26, 202 | "metadata": {}, 203 | "output_type": "execute_result" 204 | } 205 | ], 206 | "source": [ 207 | "data.iloc[1]['full_text']" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 27, 213 | "metadata": {}, 214 | "outputs": [ 215 | { 216 | "data": { 217 | "text/plain": [ 218 | "'us unveils world powerful supercomputer beat china us unveil world powerful supercomputer call summit beat previous record holder china sunway taihulight peak performance trillion calculation per second twice fast sunway taihulight capable trillion calculation per second summit server reportedly take size two tennis court'" 219 | ] 220 | }, 221 | "execution_count": 27, 222 | "metadata": {}, 223 | "output_type": "execute_result" 224 | } 225 | ], 226 | "source": [ 227 | "data.iloc[1]['clean_text']" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": 28, 233 | "metadata": {}, 234 | "outputs": [ 235 | { 236 | "data": { 237 | "text/plain": [ 238 | "'technology'" 239 | ] 240 | }, 241 | "execution_count": 28, 242 | "metadata": {}, 243 | "output_type": "execute_result" 244 | } 245 | ], 246 | "source": [ 247 | "data.iloc[1]['news_category']" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": 29, 253 | "metadata": {}, 254 | "outputs": [], 255 | "source": [ 256 | "articles, labels = np.array(data['clean_text']), np.array(data['news_category'])" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": 32, 262 | "metadata": {}, 263 | "outputs": [], 264 | "source": [ 265 | "from sklearn.model_selection import train_test_split\n", 266 | "\n", 267 | "train_articles, test_articles, train_labels, test_labels = train_test_split(articles, labels, random_state=42, test_size=0.3)" 268 | ] 269 | }, 270 | { 271 | "cell_type": "code", 272 | "execution_count": 34, 273 | "metadata": {}, 274 | "outputs": [ 275 | { 276 | "name": "stdout", 277 | "output_type": "stream", 278 | "text": [ 279 | "Train data size: (51,) (51,)\n", 280 | "Test data size: (23,) (23,)\n" 281 | ] 282 | } 283 | ], 284 | "source": [ 285 | "print('Train data size:', train_articles.shape, train_labels.shape)\n", 286 | "print('Test data size:', test_articles.shape, test_labels.shape)" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 37, 292 | "metadata": {}, 293 | "outputs": [ 294 | { 295 | "name": "stdout", 296 | "output_type": "stream", 297 | "text": [ 298 | "Train news article categories: Counter({'world': 18, 'sports': 18, 'technology': 15})\n", 299 | "Test news article categories: Counter({'technology': 9, 'world': 7, 'sports': 7})\n" 300 | ] 301 | } 302 | ], 303 | "source": [ 304 | "from collections import Counter\n", 305 | "\n", 306 | "print('Train news article categories:', Counter(train_labels))\n", 307 | "print('Test news article categories:', Counter(test_labels))" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": 53, 313 | "metadata": {}, 314 | "outputs": [ 315 | { 316 | "data": { 317 | "text/plain": [ 318 | "(array([[0, 0, 0, ..., 0, 0, 0],\n", 319 | " [0, 0, 0, ..., 0, 0, 0],\n", 320 | " [0, 0, 0, ..., 0, 0, 0],\n", 321 | " ...,\n", 322 | " [0, 0, 0, ..., 0, 0, 0],\n", 323 | " [0, 0, 0, ..., 0, 0, 0],\n", 324 | " [0, 0, 0, ..., 0, 0, 0]], dtype=int64), (51, 957))" 325 | ] 326 | }, 327 | "execution_count": 53, 328 | "metadata": {}, 329 | "output_type": "execute_result" 330 | } 331 | ], 332 | "source": [ 333 | "from sklearn.feature_extraction.text import CountVectorizer\n", 334 | "\n", 335 | "cv = CountVectorizer(min_df=0., max_df=1.)\n", 336 | "train_features = cv.fit_transform(train_articles)\n", 337 | "train_features = train_features.toarray()\n", 338 | "train_features, train_features.shape" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": 55, 344 | "metadata": {}, 345 | "outputs": [ 346 | { 347 | "name": "stdout", 348 | "output_type": "stream", 349 | "text": [ 350 | "['abdul', 'able', 'academy', 'accept', 'access', 'accommodation', 'accord', 'account', 'accuse', 'acharya', 'act', 'active', 'add', 'address', 'adviser', 'affair', 'affect', 'africa', 'afridi', 'afternoon']\n", 351 | "Total Vocabulary size (unique words): 957\n" 352 | ] 353 | } 354 | ], 355 | "source": [ 356 | "vocabulary = cv.get_feature_names()\n", 357 | "print(vocabulary[:20])\n", 358 | "print('Total Vocabulary size (unique words):', len(vocabulary))" 359 | ] 360 | }, 361 | { 362 | "cell_type": "code", 363 | "execution_count": 45, 364 | "metadata": {}, 365 | "outputs": [ 366 | { 367 | "data": { 368 | "text/html": [ 369 | "
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" 655 | ], 656 | "text/plain": [ 657 | " abdul able academy accept access accommodation accord account \\\n", 658 | "0 0 0 0 0 0 0 0 0 \n", 659 | "1 0 0 0 0 0 0 0 0 \n", 660 | "2 0 0 0 1 0 0 0 0 \n", 661 | "3 0 0 0 0 0 0 0 1 \n", 662 | "4 0 0 0 0 0 0 1 0 \n", 663 | "5 0 0 0 0 0 0 0 0 \n", 664 | "6 0 0 0 0 0 0 0 0 \n", 665 | "7 0 0 0 0 0 0 0 0 \n", 666 | "8 0 0 0 0 1 0 0 0 \n", 667 | "9 0 0 0 0 0 0 0 0 \n", 668 | "\n", 669 | " accuse acharya ... would write wrote wuhan xi yahoo year yes \\\n", 670 | "0 0 0 ... 0 0 0 0 0 0 1 0 \n", 671 | "1 0 0 ... 0 0 0 0 0 0 0 0 \n", 672 | "2 0 0 ... 0 0 0 0 0 0 0 0 \n", 673 | "3 0 0 ... 0 0 1 0 0 0 0 0 \n", 674 | "4 0 0 ... 0 0 0 0 0 0 0 0 \n", 675 | "5 0 0 ... 0 0 0 0 0 0 0 0 \n", 676 | "6 0 0 ... 0 0 0 0 0 0 2 0 \n", 677 | "7 0 0 ... 0 0 0 0 0 0 0 0 \n", 678 | "8 0 1 ... 0 0 0 0 0 0 0 0 \n", 679 | "9 0 0 ... 0 0 0 0 0 0 0 0 \n", 680 | "\n", 681 | " young yrs \n", 682 | "0 0 0 \n", 683 | "1 0 0 \n", 684 | "2 0 0 \n", 685 | "3 0 0 \n", 686 | "4 0 0 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| "train_articles[0]" 724 | ] 725 | }, 726 | { 727 | "cell_type": "code", 728 | "execution_count": 49, 729 | "metadata": {}, 730 | "outputs": [ 731 | { 732 | "data": { 733 | "text/plain": [ 734 | "award 1\n", 735 | "become 2\n", 736 | "djokovic 2\n", 737 | "earn 3\n", 738 | "federer 1\n", 739 | "first 1\n", 740 | "follow 1\n", 741 | "french 1\n", 742 | "june 1\n", 743 | "million 4\n", 744 | "money 2\n", 745 | "nadal 2\n", 746 | "notably 1\n", 747 | "novak 1\n", 748 | "number 1\n", 749 | "old 1\n", 750 | "one 1\n", 751 | "open 1\n", 752 | "player 3\n", 753 | "prize 2\n", 754 | "rafael 1\n", 755 | "rd 1\n", 756 | "roger 1\n", 757 | "tennis 2\n", 758 | "th 1\n", 759 | "third 1\n", 760 | "title 2\n", 761 | "tournament 1\n", 762 | "win 2\n", 763 | "world 1\n", 764 | "year 1\n", 765 | "Name: 0, dtype: int64" 766 | ] 767 | }, 768 | "execution_count": 49, 769 | "metadata": {}, 770 | "output_type": "execute_result" 771 | } 772 | ], 773 | "source": [ 774 | "feature_frame.iloc[0][feature_frame.iloc[0] > 0]" 775 | ] 776 | }, 777 | { 778 | "cell_type": "code", 779 | "execution_count": 52, 780 | "metadata": {}, 781 | "outputs": [ 782 | { 783 | "data": { 784 | "text/plain": [ 785 | "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", 786 | " max_features=None, max_leaf_nodes=None,\n", 787 | " min_impurity_split=1e-07, min_samples_leaf=1,\n", 788 | " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", 789 | " presort=False, random_state=42, splitter='best')" 790 | ] 791 | }, 792 | "execution_count": 52, 793 | "metadata": {}, 794 | "output_type": "execute_result" 795 | } 796 | ], 797 | "source": [ 798 | "from sklearn.tree import DecisionTreeClassifier\n", 799 | "\n", 800 | "dt = DecisionTreeClassifier(random_state=42)\n", 801 | "dt.fit(train_features, train_labels)" 802 | ] 803 | }, 804 | { 805 | "cell_type": "code", 806 | "execution_count": 54, 807 | "metadata": {}, 808 | "outputs": [ 809 | { 810 | "data": { 811 | "text/plain": [ 812 | "(array([[0, 0, 0, ..., 0, 0, 0],\n", 813 | " [0, 0, 0, ..., 0, 0, 0],\n", 814 | " [0, 0, 0, ..., 0, 0, 0],\n", 815 | " ...,\n", 816 | " [0, 0, 0, ..., 0, 0, 0],\n", 817 | " [0, 0, 0, ..., 0, 0, 0],\n", 818 | " [0, 0, 0, ..., 0, 0, 0]], dtype=int64), (23, 957))" 819 | ] 820 | }, 821 | "execution_count": 54, 822 | "metadata": {}, 823 | "output_type": "execute_result" 824 | } 825 | ], 826 | "source": [ 827 | "test_features = cv.transform(test_articles)\n", 828 | "test_features = test_features.toarray()\n", 829 | "test_features, test_features.shape" 830 | ] 831 | }, 832 | { 833 | "cell_type": "code", 834 | "execution_count": 56, 835 | "metadata": {}, 836 | "outputs": [ 837 | { 838 | "data": { 839 | "text/plain": [ 840 | "array(['sports', 'sports', 'technology', 'world', 'sports', 'world',\n", 841 | " 'sports', 'sports', 'technology', 'world'], dtype=object)" 842 | ] 843 | }, 844 | "execution_count": 56, 845 | "metadata": {}, 846 | "output_type": "execute_result" 847 | } 848 | ], 849 | "source": [ 850 | "predictions = dt.predict(test_features)\n", 851 | "predictions[:10]" 852 | ] 853 | }, 854 | { 855 | "cell_type": "code", 856 | "execution_count": 57, 857 | "metadata": {}, 858 | "outputs": [], 859 | "source": [ 860 | "import model_evaluation_utils as meu" 861 | ] 862 | }, 863 | { 864 | "cell_type": "code", 865 | "execution_count": 59, 866 | "metadata": {}, 867 | "outputs": [ 868 | { 869 | "name": "stdout", 870 | "output_type": "stream", 871 | "text": [ 872 | "Model Performance metrics:\n", 873 | "------------------------------\n", 874 | "Accuracy: 0.6957\n", 875 | "Precision: 0.8088\n", 876 | "Recall: 0.6957\n", 877 | "F1 Score: 0.688\n", 878 | "\n", 879 | "Model Classification report:\n", 880 | "------------------------------\n", 881 | " precision recall f1-score support\n", 882 | "\n", 883 | " world 0.83 0.71 0.77 7\n", 884 | " technology 1.00 0.44 0.62 9\n", 885 | " sports 0.54 1.00 0.70 7\n", 886 | "\n", 887 | "avg / total 0.81 0.70 0.69 23\n", 888 | "\n", 889 | "\n", 890 | "Prediction Confusion Matrix:\n", 891 | "------------------------------\n", 892 | " Predicted: \n", 893 | " world technology sports\n", 894 | "Actual: world 5 0 2\n", 895 | " technology 1 4 4\n", 896 | " sports 0 0 7\n" 897 | ] 898 | } 899 | ], 900 | "source": [ 901 | "meu.display_model_performance_metrics(true_labels=test_labels, predicted_labels=predictions, classes=list(set(labels)))" 902 | ] 903 | }, 904 | { 905 | "cell_type": "code", 906 | "execution_count": 64, 907 | "metadata": {}, 908 | "outputs": [ 909 | { 910 | "data": { 911 | "text/html": [ 912 | "
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ArticleActual News CategoryPredicted News Category
0china zte pay billion fine us lift sanction chinese telecommunications equipment maker zte agree...technologysports
1us like piggy bank everyone rob trump slam nation brutal approach conducting trade country presi...worldsports
2apple may introduce touch sensitive side button watch apple reportedly replace physical button a...technologytechnology
3world cheapest phone freedom maker founder arrest maker world cheapest smartphone freedom price ...technologyworld
4th rank scotland score beat no england run scotland placed th odi ranking defeated top rank engl...sportssports
5pray us n korea summit lead peace pope francis pope franci sunday say pray upcoming summit us pr...worldworld
6man post video control iphone eye developer name matt moss post video twitter show control iphon...technologysports
7inzamam changed field let hit six sehwag former indian cricketer virender sehwag reveal pakistan...sportssports
8startup sue apple copy logo shortcut app startup named shift sue apple copy logo company shortcu...technologytechnology
9man jump death meccas grand mosque man jump roof grand mosque muslim holy city mecca friday body...worldworld
10us news channel host call trump dictator fox news show host call upcoming summit us president do...worldworld
11rashid create headache indian player srh coach sunriser hyderabad head coach tom moody say afgha...sportssports
12apple patent wearable device monitor blood pressure apple grant patent wearable cuff would measu...technologytechnology
13year old polish fan attend th fifa world cup andrzej bobowski year old polish fan set attend th ...sportssports
14android co founder startup unveils magnetic headphone jack android co founder andy rubin startup...technologysports
15google remove egg salad emoji promote diversity google emoji ux manager jennifer daniel say comp...technologysports
16singapore summit one time shoot kim jong un trump us president donald trump saturday say upcomin...worldworld
17fk trump oscar win actor robert de niro tony award oscar win actor robert de niro swore us presi...worldsports
18indian rd high wc ticket buyer non play nation indian fan buy third high number ticket behind us...sportssports
19cm grant lakh yr old shooter jr world cup uttar pradesh chief minister yogi adityanath grant lak...sportssports
20singapore spend crore trump kim summit pm lee singapore prime minister lee hsien loong sunday sa...worldworld
21uber aim patent ai system identify drunk rider uber apply patent ai system predict user drunk bo...technologytechnology
22rashid call bhogle sir early term bro afghanistan year old spinner rashid khan termed year old c...sportssports
\n", 1076 | "
" 1077 | ], 1078 | "text/plain": [ 1079 | " Article \\\n", 1080 | "0 china zte pay billion fine us lift sanction chinese telecommunications equipment maker zte agree... \n", 1081 | "1 us like piggy bank everyone rob trump slam nation brutal approach conducting trade country presi... \n", 1082 | "2 apple may introduce touch sensitive side button watch apple reportedly replace physical button a... \n", 1083 | "3 world cheapest phone freedom maker founder arrest maker world cheapest smartphone freedom price ... \n", 1084 | "4 th rank scotland score beat no england run scotland placed th odi ranking defeated top rank engl... \n", 1085 | "5 pray us n korea summit lead peace pope francis pope franci sunday say pray upcoming summit us pr... \n", 1086 | "6 man post video control iphone eye developer name matt moss post video twitter show control iphon... \n", 1087 | "7 inzamam changed field let hit six sehwag former indian cricketer virender sehwag reveal pakistan... \n", 1088 | "8 startup sue apple copy logo shortcut app startup named shift sue apple copy logo company shortcu... \n", 1089 | "9 man jump death meccas grand mosque man jump roof grand mosque muslim holy city mecca friday body... \n", 1090 | "10 us news channel host call trump dictator fox news show host call upcoming summit us president do... \n", 1091 | "11 rashid create headache indian player srh coach sunriser hyderabad head coach tom moody say afgha... \n", 1092 | "12 apple patent wearable device monitor blood pressure apple grant patent wearable cuff would measu... \n", 1093 | "13 year old polish fan attend th fifa world cup andrzej bobowski year old polish fan set attend th ... \n", 1094 | "14 android co founder startup unveils magnetic headphone jack android co founder andy rubin startup... \n", 1095 | "15 google remove egg salad emoji promote diversity google emoji ux manager jennifer daniel say comp... \n", 1096 | "16 singapore summit one time shoot kim jong un trump us president donald trump saturday say upcomin... \n", 1097 | "17 fk trump oscar win actor robert de niro tony award oscar win actor robert de niro swore us presi... \n", 1098 | "18 indian rd high wc ticket buyer non play nation indian fan buy third high number ticket behind us... \n", 1099 | "19 cm grant lakh yr old shooter jr world cup uttar pradesh chief minister yogi adityanath grant lak... \n", 1100 | "20 singapore spend crore trump kim summit pm lee singapore prime minister lee hsien loong sunday sa... \n", 1101 | "21 uber aim patent ai system identify drunk rider uber apply patent ai system predict user drunk bo... \n", 1102 | "22 rashid call bhogle sir early term bro afghanistan year old spinner rashid khan termed year old c... \n", 1103 | "\n", 1104 | " Actual News Category Predicted News Category \n", 1105 | "0 technology sports \n", 1106 | "1 world sports \n", 1107 | "2 technology technology \n", 1108 | "3 technology world \n", 1109 | "4 sports sports \n", 1110 | "5 world world \n", 1111 | "6 technology sports \n", 1112 | "7 sports sports \n", 1113 | "8 technology technology \n", 1114 | "9 world world \n", 1115 | "10 world world \n", 1116 | "11 sports sports \n", 1117 | "12 technology technology \n", 1118 | "13 sports sports \n", 1119 | "14 technology sports \n", 1120 | "15 technology sports \n", 1121 | "16 world world \n", 1122 | "17 world sports \n", 1123 | "18 sports sports \n", 1124 | "19 sports sports \n", 1125 | "20 world world \n", 1126 | "21 technology technology \n", 1127 | "22 sports sports " 1128 | ] 1129 | }, 1130 | "execution_count": 64, 1131 | "metadata": {}, 1132 | "output_type": "execute_result" 1133 | } 1134 | ], 1135 | "source": [ 1136 | "news_predictions = pd.DataFrame({'Article': test_articles, \n", 1137 | " 'Actual News Category': test_labels, \n", 1138 | " 'Predicted News Category': predictions})\n", 1139 | "\n", 1140 | "news_predictions[['Article', 'Actual News Category', 'Predicted News Category']]" 1141 | ] 1142 | }, 1143 | { 1144 | "cell_type": "code", 1145 | "execution_count": 68, 1146 | "metadata": {}, 1147 | "outputs": [ 1148 | { 1149 | "data": { 1150 | "image/png": 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CK4EnI+IugIh4Ic3Pt9sfeH+aP0/S31P5gcAU4K5UfxjwdJr3T+DKNL0QOLiSACXNBGYC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\ncjMzs8JxcjMzs8J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1151 | "text/plain": [ 1152 | "
" 1153 | ] 1154 | }, 1155 | "metadata": {}, 1156 | "output_type": "display_data" 1157 | } 1158 | ], 1159 | "source": [ 1160 | "dt_feature_importances = dt.feature_importances_\n", 1161 | "dt_feature_names, dt_feature_scores = zip(*sorted(zip(vocabulary, dt_feature_importances), \n", 1162 | " key=lambda x: x[1]))\n", 1163 | "\n", 1164 | "dt_feature_names, dt_feature_scores = dt_feature_names[-10:], dt_feature_scores[-10:]\n", 1165 | "y_position = list(range(len(dt_feature_names)))\n", 1166 | "plt.barh(y_position, dt_feature_scores, height=0.6, align='center')\n", 1167 | "plt.yticks(y_position , dt_feature_names)\n", 1168 | "plt.xlabel('Relative Importance Score')\n", 1169 | "plt.ylabel('Feature')\n", 1170 | "t = plt.title('Feature Importances for Decision Tree')" 1171 | ] 1172 | } 1173 | ], 1174 | "metadata": { 1175 | "anaconda-cloud": {}, 1176 | "kernelspec": { 1177 | "display_name": "Python [conda root]", 1178 | "language": "python", 1179 | "name": "conda-root-py" 1180 | }, 1181 | "language_info": { 1182 | "codemirror_mode": { 1183 | "name": "ipython", 1184 | "version": 3 1185 | }, 1186 | "file_extension": ".py", 1187 | "mimetype": "text/x-python", 1188 | "name": "python", 1189 | "nbconvert_exporter": "python", 1190 | "pygments_lexer": "ipython3", 1191 | "version": "3.5.2" 1192 | } 1193 | }, 1194 | "nbformat": 4, 1195 | "nbformat_minor": 2 1196 | } 1197 | -------------------------------------------------------------------------------- /tds_nlp_practitioners_guide/contractions.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Aug 01 01:11:02 2016 4 | 5 | @author: DIP 6 | """ 7 | 8 | CONTRACTION_MAP = { 9 | "ain't": "is not", 10 | "aren't": "are not", 11 | "can't": "cannot", 12 | "can't've": "cannot have", 13 | "'cause": "because", 14 | "could've": "could have", 15 | "couldn't": "could not", 16 | "couldn't've": "could not have", 17 | "didn't": "did not", 18 | "doesn't": "does not", 19 | "don't": "do not", 20 | "hadn't": "had not", 21 | "hadn't've": "had not have", 22 | "hasn't": "has not", 23 | "haven't": "have not", 24 | "he'd": "he would", 25 | "he'd've": "he would have", 26 | "he'll": "he will", 27 | "he'll've": "he he will have", 28 | "he's": "he is", 29 | "how'd": "how did", 30 | "how'd'y": "how do you", 31 | "how'll": "how will", 32 | "how's": "how is", 33 | "I'd": "I would", 34 | "I'd've": "I would have", 35 | "I'll": "I will", 36 | "I'll've": "I will have", 37 | "I'm": "I am", 38 | "I've": "I have", 39 | "i'd": "i would", 40 | "i'd've": "i would have", 41 | "i'll": "i will", 42 | "i'll've": "i will have", 43 | "i'm": "i am", 44 | "i've": "i have", 45 | "isn't": "is not", 46 | "it'd": "it would", 47 | "it'd've": "it would have", 48 | "it'll": "it will", 49 | "it'll've": "it will have", 50 | "it's": "it is", 51 | "let's": "let us", 52 | "ma'am": "madam", 53 | "mayn't": "may not", 54 | "might've": "might have", 55 | "mightn't": "might not", 56 | "mightn't've": "might not have", 57 | "must've": "must have", 58 | "mustn't": "must not", 59 | "mustn't've": "must not have", 60 | "needn't": "need not", 61 | "needn't've": "need not have", 62 | "o'clock": "of the clock", 63 | "oughtn't": "ought not", 64 | "oughtn't've": "ought not have", 65 | "shan't": "shall not", 66 | "sha'n't": "shall not", 67 | "shan't've": "shall not have", 68 | "she'd": "she would", 69 | "she'd've": "she would have", 70 | "she'll": "she will", 71 | "she'll've": "she will have", 72 | "she's": "she is", 73 | "should've": "should have", 74 | "shouldn't": "should not", 75 | "shouldn't've": "should not have", 76 | "so've": "so have", 77 | "so's": "so as", 78 | "that'd": "that would", 79 | "that'd've": "that would have", 80 | "that's": "that is", 81 | "there'd": "there would", 82 | "there'd've": "there would have", 83 | "there's": "there is", 84 | "they'd": "they would", 85 | "they'd've": "they would have", 86 | "they'll": "they will", 87 | "they'll've": "they will have", 88 | "they're": "they are", 89 | "they've": "they have", 90 | "to've": "to have", 91 | "wasn't": "was not", 92 | "we'd": "we would", 93 | "we'd've": "we would have", 94 | "we'll": "we will", 95 | "we'll've": "we will have", 96 | "we're": "we are", 97 | "we've": "we have", 98 | "weren't": "were not", 99 | "what'll": "what will", 100 | "what'll've": "what will have", 101 | "what're": "what are", 102 | "what's": "what is", 103 | "what've": "what have", 104 | "when's": "when is", 105 | "when've": "when have", 106 | "where'd": "where did", 107 | "where's": "where is", 108 | "where've": "where have", 109 | "who'll": "who will", 110 | "who'll've": "who will have", 111 | "who's": "who is", 112 | "who've": "who have", 113 | "why's": "why is", 114 | "why've": "why have", 115 | "will've": "will have", 116 | "won't": "will not", 117 | "won't've": "will not have", 118 | "would've": "would have", 119 | "wouldn't": "would not", 120 | "wouldn't've": "would not have", 121 | "y'all": "you all", 122 | "y'all'd": "you all would", 123 | "y'all'd've": "you all would have", 124 | "y'all're": "you all are", 125 | "y'all've": "you all have", 126 | "you'd": "you would", 127 | "you'd've": "you would have", 128 | "you'll": "you will", 129 | "you'll've": "you will have", 130 | "you're": "you are", 131 | "you've": "you have" 132 | } -------------------------------------------------------------------------------- /tds_nlp_practitioners_guide/model_evaluation_utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jul 31 20:05:23 2017 4 | 5 | @author: DIP 6 | """ 7 | 8 | from sklearn import metrics 9 | import numpy as np 10 | import pandas as pd 11 | import matplotlib.pyplot as plt 12 | from sklearn.preprocessing import LabelEncoder 13 | from sklearn.base import clone 14 | from sklearn.preprocessing import label_binarize 15 | from scipy import interp 16 | from sklearn.metrics import roc_curve, auc 17 | 18 | 19 | def get_metrics(true_labels, predicted_labels): 20 | 21 | print('Accuracy:', np.round( 22 | metrics.accuracy_score(true_labels, 23 | predicted_labels), 24 | 4)) 25 | print('Precision:', np.round( 26 | metrics.precision_score(true_labels, 27 | predicted_labels, 28 | average='weighted'), 29 | 4)) 30 | print('Recall:', np.round( 31 | metrics.recall_score(true_labels, 32 | predicted_labels, 33 | average='weighted'), 34 | 4)) 35 | print('F1 Score:', np.round( 36 | metrics.f1_score(true_labels, 37 | predicted_labels, 38 | average='weighted'), 39 | 4)) 40 | 41 | 42 | def train_predict_model(classifier, 43 | train_features, train_labels, 44 | test_features, test_labels): 45 | # build model 46 | classifier.fit(train_features, train_labels) 47 | # predict using model 48 | predictions = classifier.predict(test_features) 49 | return predictions 50 | 51 | 52 | def display_confusion_matrix(true_labels, predicted_labels, classes=[1,0]): 53 | 54 | total_classes = len(classes) 55 | level_labels = [total_classes*[0], list(range(total_classes))] 56 | 57 | cm = metrics.confusion_matrix(y_true=true_labels, y_pred=predicted_labels, 58 | labels=classes) 59 | cm_frame = pd.DataFrame(data=cm, 60 | columns=pd.MultiIndex(levels=[['Predicted:'], classes], 61 | labels=level_labels), 62 | index=pd.MultiIndex(levels=[['Actual:'], classes], 63 | labels=level_labels)) 64 | print(cm_frame) 65 | 66 | 67 | def display_confusion_matrix_pretty(true_labels, predicted_labels, classes=[1,0]): 68 | 69 | total_classes = len(classes) 70 | level_labels = [total_classes*[0], list(range(total_classes))] 71 | 72 | cm = metrics.confusion_matrix(y_true=true_labels, y_pred=predicted_labels, 73 | labels=classes) 74 | cm_frame = pd.DataFrame(data=cm, 75 | columns=pd.MultiIndex(levels=[['Predicted:'], classes], 76 | labels=level_labels), 77 | index=pd.MultiIndex(levels=[['Actual:'], classes], 78 | labels=level_labels)) 79 | return cm_frame 80 | 81 | def display_classification_report(true_labels, predicted_labels, classes=[1,0]): 82 | 83 | report = metrics.classification_report(y_true=true_labels, 84 | y_pred=predicted_labels, 85 | labels=classes) 86 | print(report) 87 | 88 | 89 | 90 | def display_model_performance_metrics(true_labels, predicted_labels, classes=[1,0]): 91 | print('Model Performance metrics:') 92 | print('-'*30) 93 | get_metrics(true_labels=true_labels, predicted_labels=predicted_labels) 94 | print('\nModel Classification report:') 95 | print('-'*30) 96 | display_classification_report(true_labels=true_labels, predicted_labels=predicted_labels, 97 | classes=classes) 98 | print('\nPrediction Confusion Matrix:') 99 | print('-'*30) 100 | display_confusion_matrix(true_labels=true_labels, predicted_labels=predicted_labels, 101 | classes=classes) 102 | 103 | 104 | def plot_model_decision_surface(clf, train_features, train_labels, 105 | plot_step=0.02, cmap=plt.cm.RdYlBu, 106 | markers=None, alphas=None, colors=None): 107 | 108 | if train_features.shape[1] != 2: 109 | raise ValueError("X_train should have exactly 2 columnns!") 110 | 111 | x_min, x_max = train_features[:, 0].min() - plot_step, train_features[:, 0].max() + plot_step 112 | y_min, y_max = train_features[:, 1].min() - plot_step, train_features[:, 1].max() + plot_step 113 | xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), 114 | np.arange(y_min, y_max, plot_step)) 115 | 116 | clf_est = clone(clf) 117 | clf_est.fit(train_features,train_labels) 118 | if hasattr(clf_est, 'predict_proba'): 119 | Z = clf_est.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1] 120 | else: 121 | Z = clf_est.predict(np.c_[xx.ravel(), yy.ravel()]) 122 | Z = Z.reshape(xx.shape) 123 | cs = plt.contourf(xx, yy, Z, cmap=cmap) 124 | 125 | le = LabelEncoder() 126 | y_enc = le.fit_transform(train_labels) 127 | n_classes = len(le.classes_) 128 | plot_colors = ''.join(colors) if colors else [None] * n_classes 129 | label_names = le.classes_ 130 | markers = markers if markers else [None] * n_classes 131 | alphas = alphas if alphas else [None] * n_classes 132 | for i, color in zip(range(n_classes), plot_colors): 133 | idx = np.where(y_enc == i) 134 | plt.scatter(train_features[idx, 0], train_features[idx, 1], c=color, 135 | label=label_names[i], cmap=cmap, edgecolors='black', 136 | marker=markers[i], alpha=alphas[i]) 137 | plt.legend() 138 | plt.show() 139 | 140 | 141 | def plot_model_roc_curve(clf, features, true_labels, label_encoder=None, class_names=None): 142 | 143 | ## Compute ROC curve and ROC area for each class 144 | fpr = dict() 145 | tpr = dict() 146 | roc_auc = dict() 147 | if hasattr(clf, 'classes_'): 148 | class_labels = clf.classes_ 149 | elif label_encoder: 150 | class_labels = label_encoder.classes_ 151 | elif class_names: 152 | class_labels = class_names 153 | else: 154 | raise ValueError('Unable to derive prediction classes, please specify class_names!') 155 | n_classes = len(class_labels) 156 | y_test = label_binarize(true_labels, classes=class_labels) 157 | if n_classes == 2: 158 | if hasattr(clf, 'predict_proba'): 159 | prob = clf.predict_proba(features) 160 | y_score = prob[:, prob.shape[1]-1] 161 | elif hasattr(clf, 'decision_function'): 162 | prob = clf.decision_function(features) 163 | y_score = prob[:, prob.shape[1]-1] 164 | else: 165 | raise AttributeError("Estimator doesn't have a probability or confidence scoring system!") 166 | 167 | fpr, tpr, _ = roc_curve(y_test, y_score) 168 | roc_auc = auc(fpr, tpr) 169 | plt.plot(fpr, tpr, label='ROC curve (area = {0:0.2f})' 170 | ''.format(roc_auc), 171 | linewidth=2.5) 172 | 173 | elif n_classes > 2: 174 | if hasattr(clf, 'predict_proba'): 175 | y_score = clf.predict_proba(features) 176 | elif hasattr(clf, 'decision_function'): 177 | y_score = clf.decision_function(features) 178 | else: 179 | raise AttributeError("Estimator doesn't have a probability or confidence scoring system!") 180 | 181 | for i in range(n_classes): 182 | fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) 183 | roc_auc[i] = auc(fpr[i], tpr[i]) 184 | 185 | ## Compute micro-average ROC curve and ROC area 186 | fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) 187 | roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) 188 | 189 | ## Compute macro-average ROC curve and ROC area 190 | # First aggregate all false positive rates 191 | all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) 192 | # Then interpolate all ROC curves at this points 193 | mean_tpr = np.zeros_like(all_fpr) 194 | for i in range(n_classes): 195 | mean_tpr += interp(all_fpr, fpr[i], tpr[i]) 196 | # Finally average it and compute AUC 197 | mean_tpr /= n_classes 198 | fpr["macro"] = all_fpr 199 | tpr["macro"] = mean_tpr 200 | roc_auc["macro"] = auc(fpr["macro"], tpr["macro"]) 201 | 202 | ## Plot ROC curves 203 | plt.figure(figsize=(6, 4)) 204 | plt.plot(fpr["micro"], tpr["micro"], 205 | label='micro-average ROC curve (area = {0:0.2f})' 206 | ''.format(roc_auc["micro"]), linewidth=3) 207 | 208 | plt.plot(fpr["macro"], tpr["macro"], 209 | label='macro-average ROC curve (area = {0:0.2f})' 210 | ''.format(roc_auc["macro"]), linewidth=3) 211 | 212 | for i, label in enumerate(class_labels): 213 | plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})' 214 | ''.format(label, roc_auc[i]), 215 | linewidth=2, linestyle=':') 216 | else: 217 | raise ValueError('Number of classes should be atleast 2 or more') 218 | 219 | plt.plot([0, 1], [0, 1], 'k--') 220 | plt.xlim([0.0, 1.0]) 221 | plt.ylim([0.0, 1.05]) 222 | plt.xlabel('False Positive Rate') 223 | plt.ylabel('True Positive Rate') 224 | plt.title('Receiver Operating Characteristic (ROC) Curve') 225 | plt.legend(loc="lower right") 226 | plt.show() 227 | 228 | 229 | -------------------------------------------------------------------------------- /tds_spark_sql_intro/kdd99_dataset_details.pdf: -------------------------------------------------------------------------------- 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