├── app
    ├── runtime.txt
    ├── Procfile
    ├── requirements.txt
    ├── setup.sh
    └── app.py
├── .gitignore
├── naked
    ├── funcs
    │   ├── __init__.py
    │   └── sklearn
    │   │   ├── feature_extraction
    │   │       ├── __init__.py
    │   │       └── text.py
    │   │   ├── __init__.py
    │   │   ├── preprocessing.py
    │   │   └── linear_model.py
    ├── test_naked.py
    └── __init__.py
├── pytest.ini
├── pyproject.toml
├── .github
    └── workflows
    │   └── app.yml
├── LICENSE
├── README.md
└── poetry.lock


/app/runtime.txt:
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1 | python-3.8.8
2 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | *.ipynb
2 | *.pyc
3 | dist/
4 | 


--------------------------------------------------------------------------------
/naked/funcs/__init__.py:
--------------------------------------------------------------------------------
1 | from . import sklearn
2 | 


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/app/Procfile:
--------------------------------------------------------------------------------
1 | web: sh setup.sh && streamlit run app.py
2 | 


--------------------------------------------------------------------------------
/naked/funcs/sklearn/feature_extraction/__init__.py:
--------------------------------------------------------------------------------
1 | from . import text
2 | 


--------------------------------------------------------------------------------
/app/requirements.txt:
--------------------------------------------------------------------------------
1 | git+https://github.com/MaxHalford/naked
2 | streamlit
3 | 


--------------------------------------------------------------------------------
/pytest.ini:
--------------------------------------------------------------------------------
1 | [pytest]
2 | addopts =
3 |     --doctest-modules
4 |     --doctest-glob=README.md
5 | 


--------------------------------------------------------------------------------
/naked/funcs/sklearn/__init__.py:
--------------------------------------------------------------------------------
1 | from . import feature_extraction
2 | from . import linear_model
3 | from . import preprocessing
4 | 


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/app/setup.sh:
--------------------------------------------------------------------------------
 1 | mkdir -p ~/.streamlit/
 2 | 
 3 | echo "\
 4 | [general]\n\
 5 | email = \"maxhalford25@gmail.com\"\n\
 6 | " > ~/.streamlit/credentials.toml
 7 | 
 8 | echo "\
 9 | [server]\n\
10 | headless = true\n\
11 | enableCORS = false\n\
12 | port = $PORT\n\
13 | " > ~/.streamlit/config.toml
14 | 


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
 1 | [tool.poetry]
 2 | name = "naked"
 3 | version = "0.1.0"
 4 | description = "Strip a model and only keep what matters for predicting."
 5 | authors = ["MaxHalford <maxhalford25@gmail.com>"]
 6 | license = "MIT"
 7 | 
 8 | [tool.poetry.dependencies]
 9 | python = "^3.8"
10 | scikit-learn = "^0.24.1"
11 | 
12 | [tool.poetry.dev-dependencies]
13 | pytest = "^6.2.2"
14 | streamlit = "^0.78.0"
15 | 
16 | [build-system]
17 | requires = ["poetry-core>=1.0.0"]
18 | build-backend = "poetry.core.masonry.api"
19 | 


--------------------------------------------------------------------------------
/.github/workflows/app.yml:
--------------------------------------------------------------------------------
 1 | name: App
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - main
 7 | 
 8 | jobs:
 9 |   build:
10 |     runs-on: ubuntu-latest
11 |     steps:
12 |       - uses: actions/checkout@v2
13 |       - uses: akhileshns/heroku-deploy@v3.6.8
14 |         with:
15 |           heroku_api_key: ${{ secrets.HEROKU_API_KEY }}
16 |           heroku_app_name: naked-app
17 |           heroku_email: maxhalford25@gmail.com
18 |           buildpack: heroku/python
19 |           appdir: app
20 |           healthcheck: https://naked-app.herokuapp.com/
21 |           delay: 10
22 | 


--------------------------------------------------------------------------------
/naked/funcs/sklearn/preprocessing.py:
--------------------------------------------------------------------------------
 1 | def normalizer(x, norm):
 2 |     if norm == 'l2':
 3 |         norm_val = sum(xi ** 2 for xi in x) ** .5
 4 |     elif norm == 'l1':
 5 |         norm_val = sum(abs(xi) for xi in x)
 6 |     elif norm == 'max':
 7 |         norm_val = max(abs(xi) for xi in x)
 8 | 
 9 |     return [xi / norm_val for xi in x]
10 | 
11 | 
12 | def standard_scaler(x, mean_, var_, with_mean, with_std):
13 | 
14 |     def scale(x, m, v):
15 |         if with_mean:
16 |             x -= m
17 |         if with_std:
18 |             x /= v ** .5
19 |         return x
20 | 
21 |     return [scale(xi, m, v) for xi, m, v in zip(x, mean_, var_)]
22 | 


--------------------------------------------------------------------------------
/naked/funcs/sklearn/linear_model.py:
--------------------------------------------------------------------------------
 1 | def linear_regression(x, coef_, intercept_):
 2 |     return intercept_ + sum(xi * wi for xi, wi in enumerate(coef_))
 3 | 
 4 | 
 5 | def logistic_regression(x, coef_, intercept_):
 6 |     import math
 7 | 
 8 |     logits = [
 9 |         b + sum(xi * wi for xi, wi in zip(x, w))
10 |         for w, b in zip(coef_, intercept_)
11 |     ]
12 | 
13 |     # Sigmoid activation for binary classification
14 |     if len(logits) == 1:
15 |         p_true = 1 / (1 + math.exp(-logits[0]))
16 |         return [1 - p_true, p_true]
17 | 
18 |     # Softmax activation for multi-class classification
19 |     z_max = max(logits)
20 |     exp = [math.exp(z - z_max) for z in logits]
21 |     exp_sum = sum(exp)
22 |     return [e / exp_sum for e in exp]
23 | 


--------------------------------------------------------------------------------
/naked/funcs/sklearn/feature_extraction/text.py:
--------------------------------------------------------------------------------
 1 | def tfidf_vectorizer(x, lowercase, norm, vocabulary_, idf_):
 2 | 
 3 |     import re
 4 | 
 5 |     if lowercase:
 6 |         x = x.lower()
 7 | 
 8 |     # Tokenize
 9 |     x = re.findall(r"(?u)\b\w\w+\b", x)
10 |     x = [xi for xi in x if len(xi) > 1]
11 | 
12 |     # Count term frequencies
13 |     from collections import Counter
14 |     tf = Counter(x)
15 |     total = sum(tf.values())
16 | 
17 |     # Compute the TF-IDF of each tokenized term
18 |     tfidf = [0] * len(vocabulary_)
19 |     for term, freq in tf.items():
20 |         try:
21 |             index = vocabulary_[term]
22 |         except KeyError:
23 |             continue
24 |         tfidf[index] = freq * idf_[index] / total
25 | 
26 |     # Apply normalization
27 |     if norm == 'l2':
28 |         norm_val = sum(xi ** 2 for xi in tfidf) ** .5
29 | 
30 |     return [v / norm_val for v in tfidf]
31 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2021 Max Halford
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/app/app.py:
--------------------------------------------------------------------------------
 1 | import importlib
 2 | import pickle
 3 | 
 4 | import naked
 5 | from naked import mapping
 6 | import streamlit as st
 7 | 
 8 | """
 9 | # Convert a machine learning estimator to pure Python code
10 | 
11 | This is a tool that renders a pure Python representation of a
12 | [pickled](https://docs.python.org/3/library/pickle.html) estimator. The output is just a bunch of
13 | Python functions that don't require any dependencies whatsoever. This makes it really trivial to
14 | put a machine learning model into production: you just have to copy/paste the code into your
15 | application. The code generation is done with [`naked`](https://github.com/MaxHalford/naked).
16 | 
17 | The following estimators are supported:
18 | """
19 | 
20 | listing = ""
21 | 
22 | for name, estimators in mapping.items():
23 |     lib = importlib.import_module(name)
24 |     listing += f'* {name} {lib.__version__}\n'
25 |     for estimator in estimators:
26 |         listing += f'    * {estimator}\n'
27 | 
28 | st.markdown(listing)
29 | 
30 | uploaded_file = st.file_uploader("Choose a pickled model")
31 | 
32 | if uploaded_file:
33 |     model = pickle.loads(uploaded_file.getvalue())
34 |     st.code(naked.strip(model))
35 | 


--------------------------------------------------------------------------------
/naked/test_naked.py:
--------------------------------------------------------------------------------
 1 | import math
 2 | 
 3 | import numpy as np
 4 | import pytest
 5 | from sklearn import datasets
 6 | from sklearn.linear_model import LinearRegression, LogisticRegression
 7 | from sklearn.pipeline import make_pipeline
 8 | from sklearn.preprocessing import StandardScaler
 9 | 
10 | import naked
11 | 
12 | 
13 | @pytest.mark.parametrize("model", [
14 |     LinearRegression()
15 | ])
16 | @pytest.mark.parametrize("dataset", [
17 |     datasets.make_regression(),
18 |     datasets.load_boston(return_X_y=True),
19 |     datasets.load_diabetes(return_X_y=True)
20 | ])
21 | def test_reg(model, dataset):
22 | 
23 |     X, y = dataset
24 |     model.fit(X, y)
25 |     func = naked.strip(model)
26 | 
27 |     for x, yp in zip(X, model.predict(X)):
28 |         assert math.isclose(func(x), yp)
29 | 
30 | 
31 | @pytest.mark.parametrize("model", [
32 |     make_pipeline(StandardScaler(), LogisticRegression())
33 | ])
34 | @pytest.mark.parametrize("dataset", [
35 |     # Binary
36 |     datasets.make_classification(),
37 |     datasets.load_breast_cancer(return_X_y=True),
38 |     # Multi-class
39 |     datasets.load_wine(return_X_y=True)
40 | ])
41 | def test_clf(model, dataset):
42 | 
43 |     X, y = dataset
44 |     model.fit(X, y)
45 |     func = naked.strip(model)
46 | 
47 |     for x, yp in zip(X, model.predict_proba(X)):
48 |         assert np.allclose(func(x), yp)
49 | 


--------------------------------------------------------------------------------
/naked/__init__.py:
--------------------------------------------------------------------------------
  1 | import inspect
  2 | import re
  3 | import types
  4 | 
  5 | import numpy as np
  6 | from sklearn.pipeline import Pipeline
  7 | from sklearn.utils.validation import check_is_fitted
  8 | 
  9 | from . import funcs
 10 | 
 11 | 
 12 | __all__ = ['strip', 'AVAILABLE_MODELS']
 13 | 
 14 | class Func:
 15 | 
 16 |     def __init__(self, name, code):
 17 |         self.name = name
 18 |         self.code = code
 19 |         code = compile(code, "<string>", "exec")
 20 |         self.func = types.FunctionType(code.co_consts[0], globals(), name)
 21 | 
 22 |     def __call__(self, x):
 23 |         return self.func(x)
 24 | 
 25 |     def __repr__(self):
 26 |         return self.code.replace('\n\n\n', '\n\n')
 27 | 
 28 | class FuncPipeline:
 29 | 
 30 |     def __init__(self, funcs):
 31 |         self.funcs = funcs
 32 | 
 33 |     def __call__(self, x):
 34 |         for func in self.funcs:
 35 |             x = func(x)
 36 |         return x
 37 | 
 38 |     def __repr__(self):
 39 |         code = '\n\n'.join(repr(func) for func in self.funcs)
 40 | 
 41 |         code += '\n\n'
 42 |         code += 'def pipeline(x):\n'
 43 |         for func in self.funcs:
 44 |             code += f'    x = {func.name}(x)\n'
 45 |         code += '    return x'
 46 | 
 47 |         return code
 48 | 
 49 | 
 50 | mapping = {
 51 |     'sklearn': {
 52 |         'LinearRegression': funcs.sklearn.linear_model.linear_regression,
 53 |         'LogisticRegression': funcs.sklearn.linear_model.logistic_regression,
 54 |         'Normalizer': funcs.sklearn.preprocessing.normalizer,
 55 |         'StandardScaler': funcs.sklearn.preprocessing.standard_scaler,
 56 |         'TfidfVectorizer': funcs.sklearn.feature_extraction.text.tfidf_vectorizer
 57 |     }
 58 | }
 59 | 
 60 | 
 61 | AVAILABLE = '\n'.join(
 62 |     mod + '\n' + '\n'.join(f'    {m}' for m in sorted(mapping[mod]))
 63 |     for mod in mapping
 64 | )
 65 | 
 66 | 
 67 | def handle_input_names(x):
 68 |     return [x[name] for name in names]
 69 | 
 70 | 
 71 | def make_handle_input_names(names: str) -> Func:
 72 |     code = inspect.getsource(handle_input_names)
 73 |     loc = code.splitlines()
 74 |     return Func('handle_input_names', loc[0] + f'\n    names = {names}\n' + loc[1])
 75 | 
 76 | 
 77 | def handle_output_names(x):
 78 |     return dict(zip(names, x))
 79 | 
 80 | 
 81 | def make_handle_output_names(names: str) -> Func:
 82 |     code = inspect.getsource(handle_output_names)
 83 |     loc = code.splitlines()
 84 |     return Func('handle_output_names', loc[0] + f'\n    names = {names}\n' + loc[1])
 85 | 
 86 | 
 87 | def _strip(model):
 88 | 
 89 |     # Check if the model is supported
 90 |     mod = model.__class__.__module__.split('.')[0]
 91 |     try:
 92 |         func = mapping[mod][model.__class__.__name__]
 93 |     except KeyError:
 94 |         raise KeyError(f"I don't know how to unstrip {model.__class__.__name__} from {mod}.")
 95 | 
 96 |     # The model needs to have called fit
 97 |     check_is_fitted(model)
 98 | 
 99 |     # Now we just have to edit the function's source code by inserting the parameters
100 |     code = inspect.getsource(func)
101 |     code = re.sub(r'\(.+\)', '(x)', code, count=1)
102 | 
103 |     params_code = ''
104 | 
105 |     for param_name in inspect.signature(func).parameters:
106 |         if param_name == 'x':
107 |             continue
108 | 
109 |         param_val = getattr(model, param_name)
110 |         if isinstance(param_val, np.ndarray):
111 |             param_val = param_val.tolist()
112 |         if isinstance(param_val, str):
113 |             param_val = f"'{param_val}'"
114 | 
115 |         params_code += f'    {param_name} = {param_val}\n'
116 | 
117 |     # Insert the parameter specification code
118 |     loc = code.splitlines()
119 |     code = loc[0] + '\n\n' + params_code + '\n' + '\n'.join(loc[1:])
120 | 
121 |     return Func(func.__name__, code)
122 | 
123 | 
124 | def strip(model, input_names=None, output_names=None):
125 | 
126 |     if isinstance(model, Pipeline):
127 |         func = FuncPipeline([_strip(step) for _, step in model.steps])
128 |     else:
129 |         func = _strip(model)
130 | 
131 |     # If input names are specified, then we'll assume that the input x is a dictionary and not a
132 |     # list. We'll be able to handle by first mapping the dictionary values to a list in the
133 |     # specified order.
134 |     if input_names is not None:
135 |         handle_input_names = make_handle_input_names(input_names)
136 |         if isinstance(func, FuncPipeline):
137 |             func.funcs.insert(0, handle_input_names)
138 |         else:
139 |             func = FuncPipeline([handle_input_names, func])
140 | 
141 |     # If output names are specified, then we'll produce a dictionary instead of a list.
142 |     if output_names is not None:
143 |         handle_output_names = make_handle_output_names(output_names)
144 |         if isinstance(func, FuncPipeline):
145 |             func.funcs.append(handle_output_names)
146 |         else:
147 |             func = FuncPipeline([func, handle_output_names])
148 | 
149 |     return func
150 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | <h1>naked</h1>
  2 | 
  3 | `naked` is a Python tool which allows you to strip a model and only keep what matters for making predictions. The result is a pure Python function with no third-party dependencies that you can simply copy/paste wherever you wish.
  4 | 
  5 | This is simpler than deploying an API endpoint or loading a serialized model. The jury is still out on whether this is sane or not. Of course I'm not the first one to have done this, for instance see [sklearn-porter](https://github.com/nok/sklearn-porter) and [pure-predict](https://github.com/Ibotta/pure-predict). Note if you don't mind installing depencencies on your inference machine, then tools such as [scikit-learn-intelex](https://github.com/intel/scikit-learn-intelex) might do the job for you. It's also worth mentioning [xgb2sql](https://github.com/Chryzanthemum/xgb2sql), which converts an XGBoost model to a SQL query.
  6 | 
  7 | Note that you can use `naked` via this [web interface](https://naked-app.herokuapp.com/).
  8 | 
  9 | - [Installation](#installation)
 10 | - [Examples](#examples)
 11 |   - [`sklearn.linear_model.LinearRegression`](#sklearnlinear_modellinearregression)
 12 |   - [`sklearn.pipeline.Pipeline`](#sklearnpipelinepipeline)
 13 | - [FAQ](#faq)
 14 |   - [What models are supported?](#what-models-are-supported)
 15 |   - [Will this work for all library versions?](#will-this-work-for-all-library-versions)
 16 |   - [How can I trust this is correct?](#how-can-i-trust-this-is-correct)
 17 |   - [How should I handle feature names?](#how-should-i-handle-feature-names)
 18 |   - [What about output names?](#what-about-output-names)
 19 | - [Development workflow](#development-workflow)
 20 | - [Things to do](#things-to-do)
 21 | - [License](#license)
 22 | 
 23 | ## Installation
 24 | 
 25 | ```sh
 26 | pip install git+https://github.com/MaxHalford/naked
 27 | ```
 28 | 
 29 | ## Examples
 30 | 
 31 | ### `sklearn.linear_model.LinearRegression`
 32 | 
 33 | First, we fit a model.
 34 | 
 35 | ```py
 36 | import numpy as np
 37 | from sklearn.linear_model import LinearRegression
 38 | 
 39 | X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
 40 | y = np.dot(X, np.array([1, 2])) + 3
 41 | lin_reg = LinearRegression().fit(X, y)
 42 | lin_reg.fit(X, y)
 43 | ```
 44 | 
 45 | Then, we strip it.
 46 | 
 47 | ```py
 48 | import naked
 49 | 
 50 | print(naked.strip(lin_reg))
 51 | ```
 52 | 
 53 | Which produces the following output.
 54 | 
 55 | ```py
 56 | def linear_regression(x):
 57 | 
 58 |     coef_ = [1.0000000000000002, 1.9999999999999991]
 59 |     intercept_ = 3.0000000000000018
 60 | 
 61 |     return intercept_ + sum(xi * wi for xi, wi in enumerate(coef_))
 62 | ```
 63 | 
 64 | ### `sklearn.pipeline.Pipeline`
 65 | 
 66 | ```py
 67 | import naked
 68 | from sklearn import linear_model
 69 | from sklearn import feature_extraction
 70 | from sklearn import pipeline
 71 | from sklearn import preprocessing
 72 | 
 73 | model = pipeline.make_pipeline(
 74 |     feature_extraction.text.TfidfVectorizer(),
 75 |     preprocessing.Normalizer(),
 76 |     linear_model.LogisticRegression(solver='liblinear')
 77 | )
 78 | 
 79 | docs = ['Sad', 'Angry', 'Happy', 'Joyful']
 80 | is_positive = [False, False, True, True]
 81 | 
 82 | model.fit(docs, is_positive)
 83 | 
 84 | print(naked.strip(model))
 85 | ```
 86 | 
 87 | This produces the following output.
 88 | 
 89 | ```py
 90 | def tfidf_vectorizer(x):
 91 | 
 92 |     lowercase = True
 93 |     norm = 'l2'
 94 |     vocabulary_ = {'sad': 3, 'angry': 0, 'happy': 1, 'joyful': 2}
 95 |     idf_ = [1.916290731874155, 1.916290731874155, 1.916290731874155, 1.916290731874155]
 96 | 
 97 |     import re
 98 | 
 99 |     if lowercase:
100 |         x = x.lower()
101 | 
102 |     # Tokenize
103 |     x = re.findall(r"(?u)\b\w\w+\b", x)
104 |     x = [xi for xi in x if len(xi) > 1]
105 | 
106 |     # Count term frequencies
107 |     from collections import Counter
108 |     tf = Counter(x)
109 |     total = sum(tf.values())
110 | 
111 |     # Compute the TF-IDF of each tokenized term
112 |     tfidf = [0] * len(vocabulary_)
113 |     for term, freq in tf.items():
114 |         try:
115 |             index = vocabulary_[term]
116 |         except KeyError:
117 |             continue
118 |         tfidf[index] = freq * idf_[index] / total
119 | 
120 |     # Apply normalization
121 |     if norm == 'l2':
122 |         norm_val = sum(xi ** 2 for xi in tfidf) ** .5
123 | 
124 |     return [v / norm_val for v in tfidf]
125 | 
126 | def normalizer(x):
127 | 
128 |     norm = 'l2'
129 | 
130 |     if norm == 'l2':
131 |         norm_val = sum(xi ** 2 for xi in x) ** .5
132 |     elif norm == 'l1':
133 |         norm_val = sum(abs(xi) for xi in x)
134 |     elif norm == 'max':
135 |         norm_val = max(abs(xi) for xi in x)
136 | 
137 |     return [xi / norm_val for xi in x]
138 | 
139 | def logistic_regression(x):
140 | 
141 |     coef_ = [[-0.40105811611957726, 0.40105811611957726, 0.40105811611957726, -0.40105811611957726]]
142 |     intercept_ = [0.0]
143 | 
144 |     import math
145 | 
146 |     logits = [
147 |         b + sum(xi * wi for xi, wi in zip(x, w))
148 |         for w, b in zip(coef_, intercept_)
149 |     ]
150 | 
151 |     # Sigmoid activation for binary classification
152 |     if len(logits) == 1:
153 |         p_true = 1 / (1 + math.exp(-logits[0]))
154 |         return [1 - p_true, p_true]
155 | 
156 |     # Softmax activation for multi-class classification
157 |     z_max = max(logits)
158 |     exp = [math.exp(z - z_max) for z in logits]
159 |     exp_sum = sum(exp)
160 |     return [e / exp_sum for e in exp]
161 | 
162 | def pipeline(x):
163 |     x = tfidf_vectorizer(x)
164 |     x = normalizer(x)
165 |     x = logistic_regression(x)
166 |     return x
167 | ```
168 | 
169 | ## FAQ
170 | 
171 | ### What models are supported?
172 | 
173 | ```py
174 | >>> import naked
175 | >>> print(naked.AVAILABLE)
176 | sklearn
177 |     LinearRegression
178 |     LogisticRegression
179 |     Normalizer
180 |     StandardScaler
181 |     TfidfVectorizer
182 | 
183 | ```
184 | 
185 | ### Will this work for all library versions?
186 | 
187 | Not by design. A release of `naked` is intended to support a library above a particular version. If we notice that `naked` doesn't work for a newer version of a given library, then a new version of `naked` should be released to handle said library version. You may refer to the [`pyproject.toml`](pyproject.toml) file to view library support.
188 | 
189 | ### How can I trust this is correct?
190 | 
191 | This package is really easy to unit test. One simply has to compare the outputs of the model with its "naked" version and check that the outputs are identical. Check out the [`test_naked.py`](naked/test_naked.py) file if you're curious.
192 | 
193 | ### How should I handle feature names?
194 | 
195 | Let's take the example of a multi-class logistic regression trained on the wine dataset.
196 | 
197 | ```py
198 | from sklearn import datasets
199 | from sklearn import linear_model
200 | from sklearn import pipeline
201 | from sklearn import preprocessing
202 | 
203 | dataset = datasets.load_wine()
204 | X = dataset.data
205 | y = dataset.target
206 | model = pipeline.make_pipeline(
207 |     preprocessing.StandardScaler(),
208 |     linear_model.LogisticRegression()
209 | )
210 | model.fit(X, y)
211 | ```
212 | 
213 | By default, the `strip` function produces a function that takes as input a list of feature values. Instead, let's say we want to evaluate the function on a dictionary of features, thus associating each feature value with a name.
214 | 
215 | ```py
216 | x = dict(zip(dataset.feature_names, X[0]))
217 | print(x)
218 | ```
219 | 
220 | ```py
221 | {'alcohol': 14.23,
222 |  'malic_acid': 1.71,
223 |  'ash': 2.43,
224 |  'alcalinity_of_ash': 15.6,
225 |  'magnesium': 127.0,
226 |  'total_phenols': 2.8,
227 |  'flavanoids': 3.06,
228 |  'nonflavanoid_phenols': 0.28,
229 |  'proanthocyanins': 2.29,
230 |  'color_intensity': 5.64,
231 |  'hue': 1.04,
232 |  'od280/od315_of_diluted_wines': 3.92,
233 |  'proline': 1065.0}
234 | ```
235 | 
236 | Passing the feature names to the `strip` function will add a function that maps the features to a list.
237 | 
238 | ```py
239 | naked.strip(model, input_names=dataset.feature_names)
240 | ```
241 | 
242 | ```py
243 | def handle_input_names(x):
244 |     names = ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']
245 |     return [x[name] for name in names]
246 | 
247 | def standard_scaler(x):
248 | 
249 |     mean_ = [13.000617977528083, 2.336348314606741, 2.3665168539325854, 19.49494382022472, 99.74157303370787, 2.295112359550562, 2.0292696629213474, 0.36185393258426973, 1.5908988764044953, 5.058089882022473, 0.9574494382022468, 2.6116853932584254, 746.8932584269663]
250 |     var_ = [0.6553597304633259, 1.241004080924126, 0.07484180027774268, 11.090030614821362, 202.84332786264366, 0.3894890323191514, 0.9921135115515715, 0.015401619113748266, 0.32575424820098453, 5.344255847629093, 0.05195144969069561, 0.5012544628203511, 98609.60096578706]
251 |     with_mean = True
252 |     with_std = True
253 | 
254 |     def scale(x, m, v):
255 |         if with_mean:
256 |             x -= m
257 |         if with_std:
258 |             x /= v ** .5
259 |         return x
260 | 
261 |     return [scale(xi, m, v) for xi, m, v in zip(x, mean_, var_)]
262 | 
263 | def logistic_regression(x):
264 | 
265 |     coef_ = [[0.8101347947338147, 0.20382073148760085, 0.47221241678911957, -0.8447843882542064, 0.04952904623674445, 0.21372479616642068, 0.6478750705319883, -0.19982499112990385, 0.13833867563545404, 0.17160966151451867, 0.13090887117218597, 0.7259506896985365, 1.07895948707047], [-1.0103233753629153, -0.44045952703036084, -0.8480739967718842, 0.5835732316278703, -0.09770602368275362, 0.027527982220605866, 0.35399157401383297, 0.21278279386396404, 0.2633610495737497, -1.0412707677956505, 0.6825215991118386, 0.05287634940648419, -1.1407929345327175], [0.20018858062910203, 0.23663879554275832, 0.37586157998276365, 0.26121115662633365, 0.048176977446007865, -0.2412527783870254, -1.0018666445458222, -0.012957802734061021, -0.40169972520920566, 0.8696611062811332, -0.8134304702840255, -0.7788270391050198, 0.061833447462247046]]
266 |     intercept_ = [0.41229358315867787, 0.7048164121833935, -1.1171099953420585]
267 | 
268 |     import math
269 | 
270 |     logits = [
271 |         b + sum(xi * wi for xi, wi in zip(x, w))
272 |         for w, b in zip(coef_, intercept_)
273 |     ]
274 | 
275 |     # Sigmoid activation for binary classification
276 |     if len(logits) == 1:
277 |         p_true = 1 / (1 + math.exp(-logits[0]))
278 |         return [1 - p_true, p_true]
279 | 
280 |     # Softmax activation for multi-class classification
281 |     z_max = max(logits)
282 |     exp = [math.exp(z - z_max) for z in logits]
283 |     exp_sum = sum(exp)
284 |     return [e / exp_sum for e in exp]
285 | 
286 | def pipeline(x):
287 |     x = handle_input_names(x)
288 |     x = standard_scaler(x)
289 |     x = logistic_regression(x)
290 |     return x
291 | ```
292 | 
293 | ### What about output names?
294 | 
295 | You can also specify the `output_names` parameter to associate each output value with a name. Of course, this doesn't work for cases where a single value is produced, such as single-target regression.
296 | 
297 | 
298 | ```py
299 | naked.strip(model, input_names=dataset.feature_names, output_names=dataset.target_names)
300 | ```
301 | 
302 | ```py
303 | def handle_input_names(x):
304 |     names = ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']
305 |     return [x[name] for name in names]
306 | 
307 | def standard_scaler(x):
308 | 
309 |     mean_ = [13.000617977528083, 2.336348314606741, 2.3665168539325854, 19.49494382022472, 99.74157303370787, 2.295112359550562, 2.0292696629213474, 0.36185393258426973, 1.5908988764044953, 5.058089882022473, 0.9574494382022468, 2.6116853932584254, 746.8932584269663]
310 |     var_ = [0.6553597304633259, 1.241004080924126, 0.07484180027774268, 11.090030614821362, 202.84332786264366, 0.3894890323191514, 0.9921135115515715, 0.015401619113748266, 0.32575424820098453, 5.344255847629093, 0.05195144969069561, 0.5012544628203511, 98609.60096578706]
311 |     with_mean = True
312 |     with_std = True
313 | 
314 |     def scale(x, m, v):
315 |         if with_mean:
316 |             x -= m
317 |         if with_std:
318 |             x /= v ** .5
319 |         return x
320 | 
321 |     return [scale(xi, m, v) for xi, m, v in zip(x, mean_, var_)]
322 | 
323 | def logistic_regression(x):
324 | 
325 |     coef_ = [[0.8101347947338147, 0.20382073148760085, 0.47221241678911957, -0.8447843882542064, 0.04952904623674445, 0.21372479616642068, 0.6478750705319883, -0.19982499112990385, 0.13833867563545404, 0.17160966151451867, 0.13090887117218597, 0.7259506896985365, 1.07895948707047], [-1.0103233753629153, -0.44045952703036084, -0.8480739967718842, 0.5835732316278703, -0.09770602368275362, 0.027527982220605866, 0.35399157401383297, 0.21278279386396404, 0.2633610495737497, -1.0412707677956505, 0.6825215991118386, 0.05287634940648419, -1.1407929345327175], [0.20018858062910203, 0.23663879554275832, 0.37586157998276365, 0.26121115662633365, 0.048176977446007865, -0.2412527783870254, -1.0018666445458222, -0.012957802734061021, -0.40169972520920566, 0.8696611062811332, -0.8134304702840255, -0.7788270391050198, 0.061833447462247046]]
326 |     intercept_ = [0.41229358315867787, 0.7048164121833935, -1.1171099953420585]
327 | 
328 |     import math
329 | 
330 |     logits = [
331 |         b + sum(xi * wi for xi, wi in zip(x, w))
332 |         for w, b in zip(coef_, intercept_)
333 |     ]
334 | 
335 |     # Sigmoid activation for binary classification
336 |     if len(logits) == 1:
337 |         p_true = 1 / (1 + math.exp(-logits[0]))
338 |         return [1 - p_true, p_true]
339 | 
340 |     # Softmax activation for multi-class classification
341 |     z_max = max(logits)
342 |     exp = [math.exp(z - z_max) for z in logits]
343 |     exp_sum = sum(exp)
344 |     return [e / exp_sum for e in exp]
345 | 
346 | def handle_output_names(x):
347 |     names = ['class_0' 'class_1' 'class_2']
348 |     return dict(zip(names, x))
349 | 
350 | def pipeline(x):
351 |     x = handle_input_names(x)
352 |     x = standard_scaler(x)
353 |     x = logistic_regression(x)
354 |     x = handle_output_names(x)
355 |     return x
356 | ```
357 | 
358 | As you can see, by specifying `input_names` as well as `output_names`, we obtain a pipeline of functions which takes as input a dictionary and produces a dictionary.
359 | ## Development workflow
360 | 
361 | ```sh
362 | git clone https://github.com/MaxHalford/naked
363 | cd naked
364 | poetry install
365 | poetry shell
366 | pytest
367 | ```
368 | 
369 | You may test the web interface locally by running streamlit:
370 | 
371 | ```sh
372 | streamlit run app/app.py
373 | ```
374 | 
375 | ## Things to do
376 | 
377 | - Implement more models. For instance it should quite straightforward to support LightGBM.
378 | - Remove useless branching conditions. Parameters are currently handled via `if` statements. Ideally it would be nice to remove the `if` statements and only keep the code that will actually run. This should be doable by using the `ast` module.
379 | ## License
380 | 
381 | MIT
382 | 


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/poetry.lock:
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   4 | description = "Altair: A declarative statistical visualization library for Python."
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 161 | description = "Composable command line interface toolkit"
 162 | category = "dev"
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 305 | nbformat = ">=4.2.0"
 306 | traitlets = ">=4.3.1"
 307 | widgetsnbextension = ">=3.5.0,<3.6.0"
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 309 | [package.extras]
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 315 | description = "An autocompletion tool for Python that can be used for text editors."
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 317 | optional = false
 318 | python-versions = ">=3.6"
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 320 | [package.dependencies]
 321 | parso = ">=0.8.0,<0.9.0"
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 323 | [package.extras]
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 326 | 
 327 | [[package]]
 328 | name = "jinja2"
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 330 | description = "A very fast and expressive template engine."
 331 | category = "dev"
 332 | optional = false
 333 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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 336 | MarkupSafe = ">=0.23"
 337 | 
 338 | [package.extras]
 339 | i18n = ["Babel (>=0.8)"]
 340 | 
 341 | [[package]]
 342 | name = "joblib"
 343 | version = "1.0.1"
 344 | description = "Lightweight pipelining with Python functions"
 345 | category = "main"
 346 | optional = false
 347 | python-versions = ">=3.6"
 348 | 
 349 | [[package]]
 350 | name = "jsonschema"
 351 | version = "3.2.0"
 352 | description = "An implementation of JSON Schema validation for Python"
 353 | category = "dev"
 354 | optional = false
 355 | python-versions = "*"
 356 | 
 357 | [package.dependencies]
 358 | attrs = ">=17.4.0"
 359 | pyrsistent = ">=0.14.0"
 360 | six = ">=1.11.0"
 361 | 
 362 | [package.extras]
 363 | format = ["idna", "jsonpointer (>1.13)", "rfc3987", "strict-rfc3339", "webcolors"]
 364 | format_nongpl = ["idna", "jsonpointer (>1.13)", "webcolors", "rfc3986-validator (>0.1.0)", "rfc3339-validator"]
 365 | 
 366 | [[package]]
 367 | name = "jupyter-client"
 368 | version = "6.1.11"
 369 | description = "Jupyter protocol implementation and client libraries"
 370 | category = "dev"
 371 | optional = false
 372 | python-versions = ">=3.5"
 373 | 
 374 | [package.dependencies]
 375 | jupyter-core = ">=4.6.0"
 376 | python-dateutil = ">=2.1"
 377 | pyzmq = ">=13"
 378 | tornado = ">=4.1"
 379 | traitlets = "*"
 380 | 
 381 | [package.extras]
 382 | doc = ["sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
 383 | test = ["jedi (<=0.17.2)", "ipykernel", "ipython", "mock", "pytest", "pytest-asyncio", "async-generator", "pytest-timeout"]
 384 | 
 385 | [[package]]
 386 | name = "jupyter-core"
 387 | version = "4.7.1"
 388 | description = "Jupyter core package. A base package on which Jupyter projects rely."
 389 | category = "dev"
 390 | optional = false
 391 | python-versions = ">=3.6"
 392 | 
 393 | [package.dependencies]
 394 | pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""}
 395 | traitlets = "*"
 396 | 
 397 | [[package]]
 398 | name = "jupyterlab-pygments"
 399 | version = "0.1.2"
 400 | description = "Pygments theme using JupyterLab CSS variables"
 401 | category = "dev"
 402 | optional = false
 403 | python-versions = "*"
 404 | 
 405 | [package.dependencies]
 406 | pygments = ">=2.4.1,<3"
 407 | 
 408 | [[package]]
 409 | name = "jupyterlab-widgets"
 410 | version = "1.0.0"
 411 | description = "A JupyterLab extension."
 412 | category = "dev"
 413 | optional = false
 414 | python-versions = ">=3.6"
 415 | 
 416 | [[package]]
 417 | name = "markupsafe"
 418 | version = "1.1.1"
 419 | description = "Safely add untrusted strings to HTML/XML markup."
 420 | category = "dev"
 421 | optional = false
 422 | python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*"
 423 | 
 424 | [[package]]
 425 | name = "mistune"
 426 | version = "0.8.4"
 427 | description = "The fastest markdown parser in pure Python"
 428 | category = "dev"
 429 | optional = false
 430 | python-versions = "*"
 431 | 
 432 | [[package]]
 433 | name = "nbclient"
 434 | version = "0.5.3"
 435 | description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 436 | category = "dev"
 437 | optional = false
 438 | python-versions = ">=3.6.1"
 439 | 
 440 | [package.dependencies]
 441 | async-generator = "*"
 442 | jupyter-client = ">=6.1.5"
 443 | nbformat = ">=5.0"
 444 | nest-asyncio = "*"
 445 | traitlets = ">=4.2"
 446 | 
 447 | [package.extras]
 448 | dev = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"]
 449 | sphinx = ["Sphinx (>=1.7)", "sphinx-book-theme", "mock", "moto", "myst-parser"]
 450 | test = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"]
 451 | 
 452 | [[package]]
 453 | name = "nbconvert"
 454 | version = "6.0.7"
 455 | description = "Converting Jupyter Notebooks"
 456 | category = "dev"
 457 | optional = false
 458 | python-versions = ">=3.6"
 459 | 
 460 | [package.dependencies]
 461 | bleach = "*"
 462 | defusedxml = "*"
 463 | entrypoints = ">=0.2.2"
 464 | jinja2 = ">=2.4"
 465 | jupyter-core = "*"
 466 | jupyterlab-pygments = "*"
 467 | mistune = ">=0.8.1,<2"
 468 | nbclient = ">=0.5.0,<0.6.0"
 469 | nbformat = ">=4.4"
 470 | pandocfilters = ">=1.4.1"
 471 | pygments = ">=2.4.1"
 472 | testpath = "*"
 473 | traitlets = ">=4.2"
 474 | 
 475 | [package.extras]
 476 | all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
 477 | docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
 478 | serve = ["tornado (>=4.0)"]
 479 | test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)"]
 480 | webpdf = ["pyppeteer (==0.2.2)"]
 481 | 
 482 | [[package]]
 483 | name = "nbformat"
 484 | version = "5.1.2"
 485 | description = "The Jupyter Notebook format"
 486 | category = "dev"
 487 | optional = false
 488 | python-versions = ">=3.5"
 489 | 
 490 | [package.dependencies]
 491 | ipython-genutils = "*"
 492 | jsonschema = ">=2.4,<2.5.0 || >2.5.0"
 493 | jupyter-core = "*"
 494 | traitlets = ">=4.1"
 495 | 
 496 | [package.extras]
 497 | fast = ["fastjsonschema"]
 498 | test = ["check-manifest", "fastjsonschema", "testpath", "pytest", "pytest-cov"]
 499 | 
 500 | [[package]]
 501 | name = "nest-asyncio"
 502 | version = "1.5.1"
 503 | description = "Patch asyncio to allow nested event loops"
 504 | category = "dev"
 505 | optional = false
 506 | python-versions = ">=3.5"
 507 | 
 508 | [[package]]
 509 | name = "notebook"
 510 | version = "6.2.0"
 511 | description = "A web-based notebook environment for interactive computing"
 512 | category = "dev"
 513 | optional = false
 514 | python-versions = ">=3.5"
 515 | 
 516 | [package.dependencies]
 517 | argon2-cffi = "*"
 518 | ipykernel = "*"
 519 | ipython-genutils = "*"
 520 | jinja2 = "*"
 521 | jupyter-client = ">=5.3.4"
 522 | jupyter-core = ">=4.6.1"
 523 | nbconvert = "*"
 524 | nbformat = "*"
 525 | prometheus-client = "*"
 526 | pyzmq = ">=17"
 527 | Send2Trash = ">=1.5.0"
 528 | terminado = ">=0.8.3"
 529 | tornado = ">=6.1"
 530 | traitlets = ">=4.2.1"
 531 | 
 532 | [package.extras]
 533 | docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme"]
 534 | json-logging = ["json-logging"]
 535 | test = ["pytest", "coverage", "requests", "nbval", "selenium", "pytest-cov", "requests-unixsocket"]
 536 | 
 537 | [[package]]
 538 | name = "numpy"
 539 | version = "1.20.1"
 540 | description = "NumPy is the fundamental package for array computing with Python."
 541 | category = "main"
 542 | optional = false
 543 | python-versions = ">=3.7"
 544 | 
 545 | [[package]]
 546 | name = "packaging"
 547 | version = "20.9"
 548 | description = "Core utilities for Python packages"
 549 | category = "dev"
 550 | optional = false
 551 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 552 | 
 553 | [package.dependencies]
 554 | pyparsing = ">=2.0.2"
 555 | 
 556 | [[package]]
 557 | name = "pandas"
 558 | version = "1.2.3"
 559 | description = "Powerful data structures for data analysis, time series, and statistics"
 560 | category = "dev"
 561 | optional = false
 562 | python-versions = ">=3.7.1"
 563 | 
 564 | [package.dependencies]
 565 | numpy = ">=1.16.5"
 566 | python-dateutil = ">=2.7.3"
 567 | pytz = ">=2017.3"
 568 | 
 569 | [package.extras]
 570 | test = ["pytest (>=5.0.1)", "pytest-xdist", "hypothesis (>=3.58)"]
 571 | 
 572 | [[package]]
 573 | name = "pandocfilters"
 574 | version = "1.4.3"
 575 | description = "Utilities for writing pandoc filters in python"
 576 | category = "dev"
 577 | optional = false
 578 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 579 | 
 580 | [[package]]
 581 | name = "parso"
 582 | version = "0.8.1"
 583 | description = "A Python Parser"
 584 | category = "dev"
 585 | optional = false
 586 | python-versions = ">=3.6"
 587 | 
 588 | [package.extras]
 589 | qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
 590 | testing = ["docopt", "pytest (<6.0.0)"]
 591 | 
 592 | [[package]]
 593 | name = "pexpect"
 594 | version = "4.8.0"
 595 | description = "Pexpect allows easy control of interactive console applications."
 596 | category = "dev"
 597 | optional = false
 598 | python-versions = "*"
 599 | 
 600 | [package.dependencies]
 601 | ptyprocess = ">=0.5"
 602 | 
 603 | [[package]]
 604 | name = "pickleshare"
 605 | version = "0.7.5"
 606 | description = "Tiny 'shelve'-like database with concurrency support"
 607 | category = "dev"
 608 | optional = false
 609 | python-versions = "*"
 610 | 
 611 | [[package]]
 612 | name = "pillow"
 613 | version = "8.1.1"
 614 | description = "Python Imaging Library (Fork)"
 615 | category = "dev"
 616 | optional = false
 617 | python-versions = ">=3.6"
 618 | 
 619 | [[package]]
 620 | name = "pluggy"
 621 | version = "0.13.1"
 622 | description = "plugin and hook calling mechanisms for python"
 623 | category = "dev"
 624 | optional = false
 625 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 626 | 
 627 | [package.extras]
 628 | dev = ["pre-commit", "tox"]
 629 | 
 630 | [[package]]
 631 | name = "prometheus-client"
 632 | version = "0.9.0"
 633 | description = "Python client for the Prometheus monitoring system."
 634 | category = "dev"
 635 | optional = false
 636 | python-versions = "*"
 637 | 
 638 | [package.extras]
 639 | twisted = ["twisted"]
 640 | 
 641 | [[package]]
 642 | name = "prompt-toolkit"
 643 | version = "3.0.16"
 644 | description = "Library for building powerful interactive command lines in Python"
 645 | category = "dev"
 646 | optional = false
 647 | python-versions = ">=3.6.1"
 648 | 
 649 | [package.dependencies]
 650 | wcwidth = "*"
 651 | 
 652 | [[package]]
 653 | name = "protobuf"
 654 | version = "3.15.5"
 655 | description = "Protocol Buffers"
 656 | category = "dev"
 657 | optional = false
 658 | python-versions = "*"
 659 | 
 660 | [package.dependencies]
 661 | six = ">=1.9"
 662 | 
 663 | [[package]]
 664 | name = "ptyprocess"
 665 | version = "0.7.0"
 666 | description = "Run a subprocess in a pseudo terminal"
 667 | category = "dev"
 668 | optional = false
 669 | python-versions = "*"
 670 | 
 671 | [[package]]
 672 | name = "py"
 673 | version = "1.10.0"
 674 | description = "library with cross-python path, ini-parsing, io, code, log facilities"
 675 | category = "dev"
 676 | optional = false
 677 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 678 | 
 679 | [[package]]
 680 | name = "pyarrow"
 681 | version = "3.0.0"
 682 | description = "Python library for Apache Arrow"
 683 | category = "dev"
 684 | optional = false
 685 | python-versions = ">=3.6"
 686 | 
 687 | [package.dependencies]
 688 | numpy = ">=1.16.6"
 689 | 
 690 | [[package]]
 691 | name = "pycparser"
 692 | version = "2.20"
 693 | description = "C parser in Python"
 694 | category = "dev"
 695 | optional = false
 696 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 697 | 
 698 | [[package]]
 699 | name = "pydeck"
 700 | version = "0.6.1"
 701 | description = "Widget for deck.gl maps"
 702 | category = "dev"
 703 | optional = false
 704 | python-versions = "*"
 705 | 
 706 | [package.dependencies]
 707 | ipykernel = {version = ">=5.1.2", markers = "python_version >= \"3.4\""}
 708 | ipywidgets = ">=7.0.0"
 709 | jinja2 = ">=2.10.1"
 710 | numpy = ">=1.16.4"
 711 | traitlets = ">=4.3.2"
 712 | 
 713 | [package.extras]
 714 | testing = ["pytest"]
 715 | 
 716 | [[package]]
 717 | name = "pygments"
 718 | version = "2.8.0"
 719 | description = "Pygments is a syntax highlighting package written in Python."
 720 | category = "dev"
 721 | optional = false
 722 | python-versions = ">=3.5"
 723 | 
 724 | [[package]]
 725 | name = "pyparsing"
 726 | version = "2.4.7"
 727 | description = "Python parsing module"
 728 | category = "dev"
 729 | optional = false
 730 | python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 731 | 
 732 | [[package]]
 733 | name = "pyrsistent"
 734 | version = "0.17.3"
 735 | description = "Persistent/Functional/Immutable data structures"
 736 | category = "dev"
 737 | optional = false
 738 | python-versions = ">=3.5"
 739 | 
 740 | [[package]]
 741 | name = "pytest"
 742 | version = "6.2.2"
 743 | description = "pytest: simple powerful testing with Python"
 744 | category = "dev"
 745 | optional = false
 746 | python-versions = ">=3.6"
 747 | 
 748 | [package.dependencies]
 749 | atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""}
 750 | attrs = ">=19.2.0"
 751 | colorama = {version = "*", markers = "sys_platform == \"win32\""}
 752 | iniconfig = "*"
 753 | packaging = "*"
 754 | pluggy = ">=0.12,<1.0.0a1"
 755 | py = ">=1.8.2"
 756 | toml = "*"
 757 | 
 758 | [package.extras]
 759 | testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
 760 | 
 761 | [[package]]
 762 | name = "python-dateutil"
 763 | version = "2.8.1"
 764 | description = "Extensions to the standard Python datetime module"
 765 | category = "dev"
 766 | optional = false
 767 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 768 | 
 769 | [package.dependencies]
 770 | six = ">=1.5"
 771 | 
 772 | [[package]]
 773 | name = "pytz"
 774 | version = "2021.1"
 775 | description = "World timezone definitions, modern and historical"
 776 | category = "dev"
 777 | optional = false
 778 | python-versions = "*"
 779 | 
 780 | [[package]]
 781 | name = "pywin32"
 782 | version = "300"
 783 | description = "Python for Window Extensions"
 784 | category = "dev"
 785 | optional = false
 786 | python-versions = "*"
 787 | 
 788 | [[package]]
 789 | name = "pywinpty"
 790 | version = "0.5.7"
 791 | description = "Python bindings for the winpty library"
 792 | category = "dev"
 793 | optional = false
 794 | python-versions = "*"
 795 | 
 796 | [[package]]
 797 | name = "pyzmq"
 798 | version = "22.0.3"
 799 | description = "Python bindings for 0MQ"
 800 | category = "dev"
 801 | optional = false
 802 | python-versions = ">=3.6"
 803 | 
 804 | [package.dependencies]
 805 | cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 806 | py = {version = "*", markers = "implementation_name == \"pypy\""}
 807 | 
 808 | [[package]]
 809 | name = "requests"
 810 | version = "2.25.1"
 811 | description = "Python HTTP for Humans."
 812 | category = "dev"
 813 | optional = false
 814 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 815 | 
 816 | [package.dependencies]
 817 | certifi = ">=2017.4.17"
 818 | chardet = ">=3.0.2,<5"
 819 | idna = ">=2.5,<3"
 820 | urllib3 = ">=1.21.1,<1.27"
 821 | 
 822 | [package.extras]
 823 | security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)"]
 824 | socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
 825 | 
 826 | [[package]]
 827 | name = "scikit-learn"
 828 | version = "0.24.1"
 829 | description = "A set of python modules for machine learning and data mining"
 830 | category = "main"
 831 | optional = false
 832 | python-versions = ">=3.6"
 833 | 
 834 | [package.dependencies]
 835 | joblib = ">=0.11"
 836 | numpy = ">=1.13.3"
 837 | scipy = ">=0.19.1"
 838 | threadpoolctl = ">=2.0.0"
 839 | 
 840 | [package.extras]
 841 | benchmark = ["matplotlib (>=2.1.1)", "pandas (>=0.25.0)", "memory-profiler (>=0.57.0)"]
 842 | docs = ["matplotlib (>=2.1.1)", "scikit-image (>=0.13)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=3.2.0)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.0.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)"]
 843 | examples = ["matplotlib (>=2.1.1)", "scikit-image (>=0.13)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)"]
 844 | tests = ["matplotlib (>=2.1.1)", "scikit-image (>=0.13)", "pandas (>=0.25.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "mypy (>=0.770)", "pyamg (>=4.0.0)"]
 845 | 
 846 | [[package]]
 847 | name = "scipy"
 848 | version = "1.6.1"
 849 | description = "SciPy: Scientific Library for Python"
 850 | category = "main"
 851 | optional = false
 852 | python-versions = ">=3.7"
 853 | 
 854 | [package.dependencies]
 855 | numpy = ">=1.16.5"
 856 | 
 857 | [[package]]
 858 | name = "send2trash"
 859 | version = "1.5.0"
 860 | description = "Send file to trash natively under Mac OS X, Windows and Linux."
 861 | category = "dev"
 862 | optional = false
 863 | python-versions = "*"
 864 | 
 865 | [[package]]
 866 | name = "six"
 867 | version = "1.15.0"
 868 | description = "Python 2 and 3 compatibility utilities"
 869 | category = "dev"
 870 | optional = false
 871 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 872 | 
 873 | [[package]]
 874 | name = "smmap"
 875 | version = "3.0.5"
 876 | description = "A pure Python implementation of a sliding window memory map manager"
 877 | category = "dev"
 878 | optional = false
 879 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 880 | 
 881 | [[package]]
 882 | name = "streamlit"
 883 | version = "0.78.0"
 884 | description = "The fastest way to build data apps in Python"
 885 | category = "dev"
 886 | optional = false
 887 | python-versions = ">=3.6"
 888 | 
 889 | [package.dependencies]
 890 | altair = ">=3.2.0"
 891 | astor = "*"
 892 | base58 = "*"
 893 | blinker = "*"
 894 | cachetools = ">=4.0"
 895 | click = ">=7.0"
 896 | gitpython = "*"
 897 | numpy = "*"
 898 | packaging = "*"
 899 | pandas = ">=0.21.0"
 900 | pillow = ">=6.2.0"
 901 | protobuf = ">=3.6.0,<3.11 || >3.11"
 902 | pyarrow = {version = "*", markers = "python_version < \"3.9\""}
 903 | pydeck = ">=0.1.dev5"
 904 | python-dateutil = "*"
 905 | requests = "*"
 906 | toml = "*"
 907 | tornado = ">=5.0"
 908 | tzlocal = "*"
 909 | validators = "*"
 910 | watchdog = {version = "*", markers = "platform_system != \"Darwin\""}
 911 | 
 912 | [[package]]
 913 | name = "terminado"
 914 | version = "0.9.2"
 915 | description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 916 | category = "dev"
 917 | optional = false
 918 | python-versions = ">=3.6"
 919 | 
 920 | [package.dependencies]
 921 | ptyprocess = {version = "*", markers = "os_name != \"nt\""}
 922 | pywinpty = {version = ">=0.5", markers = "os_name == \"nt\""}
 923 | tornado = ">=4"
 924 | 
 925 | [[package]]
 926 | name = "testpath"
 927 | version = "0.4.4"
 928 | description = "Test utilities for code working with files and commands"
 929 | category = "dev"
 930 | optional = false
 931 | python-versions = "*"
 932 | 
 933 | [package.extras]
 934 | test = ["pathlib2"]
 935 | 
 936 | [[package]]
 937 | name = "threadpoolctl"
 938 | version = "2.1.0"
 939 | description = "threadpoolctl"
 940 | category = "main"
 941 | optional = false
 942 | python-versions = ">=3.5"
 943 | 
 944 | [[package]]
 945 | name = "toml"
 946 | version = "0.10.2"
 947 | description = "Python Library for Tom's Obvious, Minimal Language"
 948 | category = "dev"
 949 | optional = false
 950 | python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 951 | 
 952 | [[package]]
 953 | name = "toolz"
 954 | version = "0.11.1"
 955 | description = "List processing tools and functional utilities"
 956 | category = "dev"
 957 | optional = false
 958 | python-versions = ">=3.5"
 959 | 
 960 | [[package]]
 961 | name = "tornado"
 962 | version = "6.1"
 963 | description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 964 | category = "dev"
 965 | optional = false
 966 | python-versions = ">= 3.5"
 967 | 
 968 | [[package]]
 969 | name = "traitlets"
 970 | version = "5.0.5"
 971 | description = "Traitlets Python configuration system"
 972 | category = "dev"
 973 | optional = false
 974 | python-versions = ">=3.7"
 975 | 
 976 | [package.dependencies]
 977 | ipython-genutils = "*"
 978 | 
 979 | [package.extras]
 980 | test = ["pytest"]
 981 | 
 982 | [[package]]
 983 | name = "tzlocal"
 984 | version = "2.1"
 985 | description = "tzinfo object for the local timezone"
 986 | category = "dev"
 987 | optional = false
 988 | python-versions = "*"
 989 | 
 990 | [package.dependencies]
 991 | pytz = "*"
 992 | 
 993 | [[package]]
 994 | name = "urllib3"
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1006 | [[package]]
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1021 | [[package]]
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