├── .gitignore
├── 01-linear-regression.ipynb
├── 02-logistic-regression.ipynb
├── LICENSE
└── README.md


/.gitignore:
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130 | 


--------------------------------------------------------------------------------
/01-linear-regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Linear Regression -- Weight Confidence Intervals"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {},
 14 |    "outputs": [
 15 |     {
 16 |      "data": {
 17 |       "image/png": 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\n",
 18 |       "text/plain": [
 19 |        "<Figure size 432x360 with 9 Axes>"
 20 |       ]
 21 |      },
 22 |      "metadata": {
 23 |       "needs_background": "light"
 24 |      },
 25 |      "output_type": "display_data"
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "import matplotlib.pyplot as plt\n",
 30 |     "%matplotlib inline\n",
 31 |     "\n",
 32 |     "from mlxtend.plotting import scatterplotmatrix\n",
 33 |     "from sklearn.linear_model import LinearRegression\n",
 34 |     "from sklearn.preprocessing import StandardScaler\n",
 35 |     "from scipy import stats\n",
 36 |     "import numpy as np\n",
 37 |     "\n",
 38 |     "\n",
 39 |     "# https://en.wikipedia.org/wiki/Simple_linear_regression#Confidence_intervals\n",
 40 |     "# This data set gives average masses for women as a function of their height in a sample of American women of age 30–39. \n",
 41 |     "\n",
 42 |     "height_in_m = [1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63, 1.65, 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83]\n",
 43 |     "mass_in_kg = [52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93, 61.29, 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46]\n",
 44 |     "\n",
 45 |     "np.random.seed(0)\n",
 46 |     "rand1 = np.random.normal(size=len(height_in_m), scale=10, loc=5)\n",
 47 |     "rand2 = np.random.normal(size=len(height_in_m))\n",
 48 |     "\n",
 49 |     "X_train = np.array([(i, j, k) for i, j, k in zip(height_in_m, rand1, rand2)])\n",
 50 |     "y_train = np.array(mass_in_kg)\n",
 51 |     "\n",
 52 |     "sc_features = StandardScaler()\n",
 53 |     "sc_target = StandardScaler()\n",
 54 |     "\n",
 55 |     "X_std = sc_features.fit_transform(X_train)\n",
 56 |     "y_std = sc_target.fit_transform(y_train.reshape(-1, 1)).flatten()\n",
 57 |     "\n",
 58 |     "scatterplotmatrix(X_std, names=['Height','Rand 1', 'Rand 2'], \n",
 59 |     "                  figsize=(6, 5))\n",
 60 |     "plt.tight_layout()\n",
 61 |     "plt.show()"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "markdown",
 66 |    "metadata": {},
 67 |    "source": [
 68 |     "## Weight coefficients"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": 2,
 74 |    "metadata": {},
 75 |    "outputs": [
 76 |     {
 77 |      "data": {
 78 |       "image/png": "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\n",
 79 |       "text/plain": [
 80 |        "<Figure size 432x288 with 1 Axes>"
 81 |       ]
 82 |      },
 83 |      "metadata": {
 84 |       "needs_background": "light"
 85 |      },
 86 |      "output_type": "display_data"
 87 |     }
 88 |    ],
 89 |    "source": [
 90 |     "lr = LinearRegression()\n",
 91 |     "lr.fit(X_std, y_std)\n",
 92 |     "\n",
 93 |     "fig, ax = plt.subplots()\n",
 94 |     "ax.bar([0, 1, 2], lr.coef_)\n",
 95 |     "\n",
 96 |     "ax.set_xticks([0, 1, 2])\n",
 97 |     "ax.set_xticklabels([f'Height\\n({lr.coef_[0]:.3f})',\n",
 98 |     "                    f'Random 1\\n({lr.coef_[1]:.3f})',\n",
 99 |     "                    f'Random 2\\n({lr.coef_[2]:.3f})'])\n",
100 |     "plt.ylabel('Magnitude')\n",
101 |     "plt.show()"
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "code",
106 |    "execution_count": 3,
107 |    "metadata": {},
108 |    "outputs": [
109 |     {
110 |      "data": {
111 |       "text/plain": [
112 |        "-2.1024223302105473e-15"
113 |       ]
114 |      },
115 |      "execution_count": 3,
116 |      "metadata": {},
117 |      "output_type": "execute_result"
118 |     }
119 |    ],
120 |    "source": [
121 |     "lr.intercept_"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "code",
126 |    "execution_count": 4,
127 |    "metadata": {},
128 |    "outputs": [
129 |     {
130 |      "name": "stdout",
131 |      "output_type": "stream",
132 |      "text": [
133 |       "[65.4774427]\n"
134 |      ]
135 |     }
136 |    ],
137 |    "source": [
138 |     "# y = 0.5 in kg\n",
139 |     "print(0.5 * np.sqrt(sc_target.var_) + sc_target.mean_)"
140 |    ]
141 |   },
142 |   {
143 |    "cell_type": "code",
144 |    "execution_count": 5,
145 |    "metadata": {},
146 |    "outputs": [
147 |     {
148 |      "name": "stdout",
149 |      "output_type": "stream",
150 |      "text": [
151 |       "[72.2763281]\n"
152 |      ]
153 |     }
154 |    ],
155 |    "source": [
156 |     "# y = 1.5 in kg\n",
157 |     "print(1.5 * np.sqrt(sc_target.var_) + sc_target.mean_)"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 6,
163 |    "metadata": {},
164 |    "outputs": [
165 |     {
166 |      "name": "stdout",
167 |      "output_type": "stream",
168 |      "text": [
169 |       "[6.7988854]\n"
170 |      ]
171 |     }
172 |    ],
173 |    "source": [
174 |     "print(np.sqrt(sc_target.var_))"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "code",
179 |    "execution_count": 7,
180 |    "metadata": {},
181 |    "outputs": [
182 |     {
183 |      "data": {
184 |       "image/png": 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\n",
185 |       "text/plain": [
186 |        "<Figure size 432x288 with 1 Axes>"
187 |       ]
188 |      },
189 |      "metadata": {
190 |       "needs_background": "light"
191 |      },
192 |      "output_type": "display_data"
193 |     }
194 |    ],
195 |    "source": [
196 |     "y_pred = lr.predict(X_std)\n",
197 |     "plt.scatter(X_std[:, 0], y_std)\n",
198 |     "\n",
199 |     "x1, x2 = X_std[:, 0].argmin(), X_std[:, 0].argmax()\n",
200 |     "plt.plot([X_std[x1, 0], X_std[x2, 0]], [y_std[x1], y_std[x2]])\n",
201 |     "\n",
202 |     "plt.show()"
203 |    ]
204 |   },
205 |   {
206 |    "cell_type": "code",
207 |    "execution_count": 8,
208 |    "metadata": {},
209 |    "outputs": [],
210 |    "source": [
211 |     "def std_err_linearregression(y_true, y_pred, x):\n",
212 |     "    n = len(y_true)\n",
213 |     "    mse = np.sum((y_true - y_pred)**2) / (n-2)\n",
214 |     "    std_err = (np.sqrt(mse) / np.sqrt(np.sum((x - np.mean(x, axis=0))**2, axis=0)))\n",
215 |     "    return std_err\n",
216 |     "\n",
217 |     "\n",
218 |     "def weight_intervals(n, weight, std_err, alpha=0.05):\n",
219 |     "    t_value = stats.t.ppf(1 - alpha/2, df=n - 2)\n",
220 |     "    temp = t_value * std_err\n",
221 |     "    lower = weight - temp\n",
222 |     "    upper = weight + temp\n",
223 |     "\n",
224 |     "    return lower, upper"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "code",
229 |    "execution_count": 9,
230 |    "metadata": {},
231 |    "outputs": [],
232 |    "source": [
233 |     "y_pred = lr.predict(X_std)\n",
234 |     "\n",
235 |     "std_err = std_err_linearregression(y_std, y_pred, X_std)\n",
236 |     "\n",
237 |     "lower, upper = weight_intervals(len(y_std), lr.coef_, std_err)"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 25,
243 |    "metadata": {},
244 |    "outputs": [
245 |     {
246 |      "data": {
247 |       "image/png": 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\n",
248 |       "text/plain": [
249 |        "<Figure size 432x288 with 1 Axes>"
250 |       ]
251 |      },
252 |      "metadata": {
253 |       "needs_background": "light"
254 |      },
255 |      "output_type": "display_data"
256 |     }
257 |    ],
258 |    "source": [
259 |     "fig, ax = plt.subplots()\n",
260 |     "\n",
261 |     "ax.hlines(0, xmin=-0.1, xmax=2.2, linestyle='dashed', color='skyblue')\n",
262 |     "ax.errorbar([0, 1, 2], lr.coef_, yerr=upper - lr.coef_, fmt='.k')\n",
263 |     "\n",
264 |     "ax.set_xticks([0, 1, 2])\n",
265 |     "ax.set_xticklabels([f'Height\\n({lr.coef_[0]:.3f})',\n",
266 |     "                    f'Random 1\\n({lr.coef_[1]:.3f})',\n",
267 |     "                    f'Random 2\\n({lr.coef_[2]:.3f})'])\n",
268 |     "plt.ylabel('Magnitude');"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": null,
274 |    "metadata": {},
275 |    "outputs": [],
276 |    "source": [
277 |     "lower, upper"
278 |    ]
279 |   },
280 |   {
281 |    "cell_type": "markdown",
282 |    "metadata": {},
283 |    "source": [
284 |     "---"
285 |    ]
286 |   },
287 |   {
288 |    "cell_type": "code",
289 |    "execution_count": 33,
290 |    "metadata": {},
291 |    "outputs": [],
292 |    "source": [
293 |     "import statsmodels.api as sm\n",
294 |     "\n",
295 |     "mod = sm.OLS(y_std, X_std)\n",
296 |     "res = mod.fit()\n",
297 |     "lower, upper = res.conf_int(0.05)[:, 0], res.conf_int(0.05)[:, 1]"
298 |    ]
299 |   },
300 |   {
301 |    "cell_type": "code",
302 |    "execution_count": 34,
303 |    "metadata": {},
304 |    "outputs": [
305 |     {
306 |      "data": {
307 |       "text/plain": [
308 |        "(array([ 0.93337272, -0.03899289, -0.03548572]),\n",
309 |        " array([1.06536057, 0.09297103, 0.09006354]))"
310 |       ]
311 |      },
312 |      "execution_count": 34,
313 |      "metadata": {},
314 |      "output_type": "execute_result"
315 |     }
316 |    ],
317 |    "source": [
318 |     "lower, upper"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "code",
323 |    "execution_count": 35,
324 |    "metadata": {},
325 |    "outputs": [
326 |     {
327 |      "data": {
328 |       "image/png": 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\n",
329 |       "text/plain": [
330 |        "<Figure size 432x288 with 1 Axes>"
331 |       ]
332 |      },
333 |      "metadata": {
334 |       "needs_background": "light"
335 |      },
336 |      "output_type": "display_data"
337 |     }
338 |    ],
339 |    "source": [
340 |     "fig, ax = plt.subplots()\n",
341 |     "\n",
342 |     "ax.hlines(0, xmin=-0.1, xmax=2.2, linestyle='dashed', color='skyblue')\n",
343 |     "ax.errorbar([0, 1, 2], res.params, yerr=upper - res.params, fmt='.k')\n",
344 |     "\n",
345 |     "ax.set_xticks([0, 1, 2])\n",
346 |     "ax.set_xticklabels([f'Height\\n({lr.coef_[0]:.3f})',\n",
347 |     "                    f'Random 1\\n({lr.coef_[1]:.3f})',\n",
348 |     "                    f'Random 2\\n({lr.coef_[2]:.3f})'])\n",
349 |     "plt.ylabel('Magnitude');"
350 |    ]
351 |   },
352 |   {
353 |    "cell_type": "code",
354 |    "execution_count": null,
355 |    "metadata": {},
356 |    "outputs": [],
357 |    "source": []
358 |   }
359 |  ],
360 |  "metadata": {
361 |   "kernelspec": {
362 |    "display_name": "Python 3",
363 |    "language": "python",
364 |    "name": "python3"
365 |   },
366 |   "language_info": {
367 |    "codemirror_mode": {
368 |     "name": "ipython",
369 |     "version": 3
370 |    },
371 |    "file_extension": ".py",
372 |    "mimetype": "text/x-python",
373 |    "name": "python",
374 |    "nbconvert_exporter": "python",
375 |    "pygments_lexer": "ipython3",
376 |    "version": "3.7.1"
377 |   }
378 |  },
379 |  "nbformat": 4,
380 |  "nbformat_minor": 4
381 | }
382 | 


--------------------------------------------------------------------------------
/02-logistic-regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Logistic Regression -- Weight Confidence Intervals"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {},
 14 |    "outputs": [
 15 |     {
 16 |      "data": {
 17 |       "image/png": 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\n",
 18 |       "text/plain": [
 19 |        "<Figure size 432x360 with 9 Axes>"
 20 |       ]
 21 |      },
 22 |      "metadata": {
 23 |       "needs_background": "light"
 24 |      },
 25 |      "output_type": "display_data"
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "import matplotlib.pyplot as plt\n",
 30 |     "%matplotlib inline\n",
 31 |     "\n",
 32 |     "from mlxtend.plotting import scatterplotmatrix\n",
 33 |     "from sklearn.linear_model import LogisticRegression\n",
 34 |     "from sklearn.preprocessing import StandardScaler\n",
 35 |     "from sklearn.datasets import load_iris\n",
 36 |     "from scipy import stats\n",
 37 |     "import numpy as np\n",
 38 |     "\n",
 39 |     "\n",
 40 |     "iris = load_iris()\n",
 41 |     "\n",
 42 |     "\n",
 43 |     "X_train, y_train = iris.data[50:150, :3], iris.target[50:150]\n",
 44 |     "y_train = np.array(50*[0] + 50*[1])\n",
 45 |     "\n",
 46 |     "sc_features = StandardScaler()\n",
 47 |     "sc_target = StandardScaler()\n",
 48 |     "\n",
 49 |     "X_std = sc_features.fit_transform(X_train)\n",
 50 |     "\n",
 51 |     "fig, axes = scatterplotmatrix(X_std[y_train==0], figsize=(6, 5), alpha=0.5)\n",
 52 |     "fig, axes = scatterplotmatrix(X_std[y_train==1], fig_axes=(fig, axes), alpha=0.5,\n",
 53 |     "                              names=['Sepal Length (std.)','Sepal Width (std.)', 'Petal Length (std.)'])\n",
 54 |     "\n",
 55 |     "plt.tight_layout()\n",
 56 |     "plt.show()"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 2,
 62 |    "metadata": {},
 63 |    "outputs": [],
 64 |    "source": [
 65 |     "import pandas as pd\n",
 66 |     "\n",
 67 |     "df1 = pd.DataFrame(X_std)\n",
 68 |     "df2 = pd.DataFrame(y_train)\n",
 69 |     "\n",
 70 |     "df = pd.concat((df1, df2), axis=1)\n",
 71 |     "df.columns = ['sepal length', 'sepal width', 'petal length', 'species']\n",
 72 |     "\n",
 73 |     "df.to_csv('data.csv', index=None)"
 74 |    ]
 75 |   },
 76 |   {
 77 |    "cell_type": "markdown",
 78 |    "metadata": {},
 79 |    "source": [
 80 |     "## Weight coefficients"
 81 |    ]
 82 |   },
 83 |   {
 84 |    "cell_type": "code",
 85 |    "execution_count": 3,
 86 |    "metadata": {},
 87 |    "outputs": [
 88 |     {
 89 |      "data": {
 90 |       "image/png": 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eXtPKHpTkc0m+k+TtW/QsLV5fAh4KkOSo9rq+JMn7kiyb7vWb5BOtTi9LctzmHDTJDkk+0Orh4iSHt/Jjkpw+3fOe5Nj2nlvb3j/vTvIE4DDgHS3G32irP2/qe3RRqCr/pvwBOwKXAN8G3gMc3Mq3ofuWt7LNPx/4QJteC/xDm/4tYH2b3hlY3qYPBU5r008BPjXNse9RPsZx/6pNPwM4u02/Dnhfm34UcDuwus3fOrLvVW3Zfm3+n4Cj+q6DJVp3q4Cr2/QpwCOAc4GdgBOAN7dlxwDvbtNnAL/Xpl+2sW6mHqNtczVwX2BbuiFR9uz7+Z6nOtv4Py8HPgn8AfBI4P8C27Rl7xl5nm6dsv392+N2wHpg1zZ/DbBimuPdoxz4842ve2CX9vrZYabnHXhQ28/922vrSyN1+o/AESP7Xss079HF8Lcc3UNV3ZrkQODJwG8DH21ttuvoPkzPSgKwDLh+ZNNT2vZfTLJzkl3o3vwnJdkHKLoXy6Z6+BzHPb09Xkj3IQTwJOBdLZ71SS6dZf/frapLptnHktNn3VXVNUnunWQ3ug//K4ELgMfSnRH8z2k2eyLw3Db9IeBtsxzinKq6GSDJ5cCD2TqaJrdLsvH19yXg/cBxwIHABa2+tgNumGH7VyZ5TpveE9gH+LdNjOE/AocleV2b3xbYq01P97yvAL5QVT9q5R8DHjbL/qd7j/bOBDCDqrqDLnOvTfJN4Gi6yrusqh4/02bTzL8FOLeqnpNkVdvnpsocx/1le7yDu+o0m7D/X45M30H3Zluyeq67rwJHANdXVSX5Gt2H/EHA18Y89kym1tPW8v79eVXtN1qQ7lP/pKp6w2wbJnkK3dnZ46vqZ0nW0n14b6oAz62qK6fs/7FM/7xvyvsLpn+P9s4+gGkkeXj71rfRfnSnflcCK9N1NJJkmyT/fmS957fyJwE3t28N9wWua8uP2cyQ5jrudL4M/Je2/r7Ao0eW3ZZkc85EFr1FUHfnAa+hSwS0x98D/qWqfjzD+i9o0y8aKb+F7gxkqM4BjkjyAIAk90/y4LZs9PV7X+Cm9uH/COBxm3m8zwOvaImHJPvPsf7XgYOT3C/Jcu46i4MlVHcmgOntSHfqf3lrOtkXeFNV/Yru293bknyDrq35CSPb3ZTkK3Sdf8e2srcDf5HkPLpmh3E8Ncm1G//oToVnO+503kP3gXcp8HrgUuDmtmwNcGnu6gTemvRdd+fRXe3zVYCqur5t+5UZ1n8V8LIkF9B9mG10KXB7km+MdAIPRlVdDvwxcGarx7PoOvjh7q/fzwHL2zpvYeazrKkuHXmPvbNtu00rX9/mZ4vvOrp+g/OBs4HLuev99RHgj1pn8m/MsItFwaEg5kk79XxdVa3rOxaAJMvoOtB+0V6E5wAPax+EGrHY6k5LQ5IdW5/TcuDjdBcVfLzvuDbFommL0rzbHji3nSoH+AM//KV59aYkh9L1OZwJfKLneDaZZwCSNFD2AUjSQJkA5lmS7dLdbbpsSvlrN3ZMJjln5IqGqduvTXJlu4vwko1XQYwsPyJJpY0tkmTXJOcmuTVTxopJcnaS+833/7g1maW+7pPuDuqr0t1FvWqabfdsz/0V6e5CfdXIsue1sl9nZBwY62vLjNZXurtzf5wp4zKlu7v6/HR37n40yb2n2c82SU5K8s1Wf28YWfb09h68KiNjNs203yQvT/KSSf7fk2ICmH8vBU5v16KPupjuTtzHAKfSXWEykxdVNzbKflV1580vSXYCXkl35cFGvwD+hO7O36k+BPz3zfgfhmSm+jqW7vLChwJ/zfQ3aN0O/GFVPZLu8sOX5a5BwNYD/xn44pRtrK8tM1pf7wBePM06bwP+uqr2AW7irqu6Rj0PuE9VPZruKrv/lm4AvmXA3wG/S3cF2ZEjdTrTfj9A975cckwA8+9FdLez301VnVtVP2uzXwP22Ix9v4UucfxiZL8/raovj5aNOAM4cjOOMyTT1hdwOHBSmz6V7tLcu938U1XXV9VFbfoW4Apg9zZ/xdSbilq59bVl7qyvqjqH7pr7O7U6OoSuzqCrw2dPs58CdmhX8GxHNwjdT+hu2Luqqq5uF018BDh8tv229/U1aWNILSUmgHnUTgn3rqpr5lj1WOCzsyz/YGv++ZONHzrpbkzZs6rGHiq6qm4C7pNk13G3GZI56mt32jALVXU73TXeMz6PrYlof+5+drZJrK/Zjfn+2hX4caszgGtpSXmKU4Gf0g0H8n3gL9uwDnfW+5Tt59rvOrrhR5YULwOdXyuA6e72vFO68eFXAwfPsMqLquq61txzGvDiJB+ma4Y4ZjNiuoFu4KpNHRtlCGarr+lu9Z/2krkkO9LV1aur6idbGJP1NbM531+MX28H0Q3L8CDgfsCXkpw9y/Zz7fcGuvGflhTPAObXz2njkCR568aO3I0L2zXDJwCHVRuueKp2h+HGJoX/Q/dC3YluILO1Sa6ha28+I+P9yMS2LS7d02z1dS3dwGK0ZoL7Aj+auoN2n8VpwMlVdfrU5ZvB+prZnfU1ixuBXVqdQdfU+sNp1nsh8Lmquq31s51H98Xsznqfsv1c+12S9WYCmEftFH5Zkm2r6oSNHblwZxPO++g+/Kcd1TDdr0itaNPb0P2wyfqqurmqVlTVqqpaRdeHcNhcd6625qPd6Iat1RSz1Rdde/zRbfoI4J9ryk0z7fl9P3BFVb1zS+OxvmY3Wl+zrFN0Q3Af0YqOZvo+nu8Dh6SzA92Xqm/Rjd66T7vi59504zSdMcZ+H0bX8b+0bOl40v7dY6zx9wOHTlN+NvCvdGPQXEL3otq47JL2uAPdqJWXApfRDee8bJp9raWN7d/mr6H7dnor3TeYfVv5atoY9v5tcn1tC3wMuIpu4K+9W/mDgM+06SfRNQNcOlKvz2jLntPq4pet3j9vfc1vfdENHb2B7pv3tcDvtPK9W51d1erwPq38MO76TYYd27LL6Mbx+aORYzyD7vcA/h9wwkj5tPttyy5imt8eWOx/3gk8z9o3/ddW1XSXpy10LO+iSzTn9B3LYmV9LS2Lqb42WowxjcsmoHlWVRfTjcEz7uiRk7TeD5PZWV9LyyKrr41W0N3bseR4BiBJA+UZgCQNlAlAkgbKBCBJA2UCkKSBMgFI0kD9f48bbI6NON7CAAAAAElFTkSuQmCC\n",
 91 |       "text/plain": [
 92 |        "<Figure size 432x288 with 1 Axes>"
 93 |       ]
 94 |      },
 95 |      "metadata": {
 96 |       "needs_background": "light"
 97 |      },
 98 |      "output_type": "display_data"
 99 |     }
100 |    ],
101 |    "source": [
102 |     "lor = LogisticRegression(random_state=0, solver='newton-cg', C=1e8)\n",
103 |     "\n",
104 |     "# set C=1e8 to negate regularization to allow comparison with\n",
105 |     "# statsmodel coefficients later\n",
106 |     "\n",
107 |     "lor.fit(X_std, y_train)\n",
108 |     "\n",
109 |     "fig, ax = plt.subplots()\n",
110 |     "ax.bar([0, 1, 2], lor.coef_.flatten())\n",
111 |     "\n",
112 |     "ax.set_xticks([0, 1, 2])\n",
113 |     "ax.set_xticklabels([f'Sepal Length\\n({lor.coef_.flatten()[0]:.3f})',\n",
114 |     "                    f'Sepal Width\\n({lor.coef_.flatten()[1]:.3f})',\n",
115 |     "                    f'Petal Length\\n({lor.coef_.flatten()[2]:.3f})'])\n",
116 |     "plt.ylabel('Magnitude')\n",
117 |     "plt.show()"
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "code",
122 |    "execution_count": 4,
123 |    "metadata": {},
124 |    "outputs": [
125 |     {
126 |      "name": "stdout",
127 |      "output_type": "stream",
128 |      "text": [
129 |       "train accuracy 0.95\n"
130 |      ]
131 |     }
132 |    ],
133 |    "source": [
134 |     "print('train accuracy', lor.score(X_std, y_train))"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": 5,
140 |    "metadata": {},
141 |    "outputs": [],
142 |    "source": [
143 |     "def std_err_logisticregression(y_true, y_pred_proba, X):\n",
144 |     "    # based on code from \n",
145 |     "    # https://stats.stackexchange.com/questions/89484/how-to-compute-the-standard-errors-of-a-logistic-regressions-coefficients\n",
146 |     "\n",
147 |     "    # Design matrix -- add column of 1's at the beginning of your X_train matrix\n",
148 |     "    X_design = np.hstack([np.ones((X.shape[0], 1)), X])\n",
149 |     "    \n",
150 |     "    # Initiate matrix of 0's, fill diagonal with each predicted observation's variance\n",
151 |     "    V = np.diagflat(np.product(y_pred_proba, axis=1))\n",
152 |     "\n",
153 |     "    # Covariance matrix\n",
154 |     "    cov = np.linalg.inv(X_design.T @ V @ X_design)\n",
155 |     "\n",
156 |     "    # Standard errors:\n",
157 |     "    std_errs = np.sqrt(np.diag(cov))\n",
158 |     "    \n",
159 |     "    return std_errs\n",
160 |     "\n",
161 |     "\n",
162 |     "def weight_intervals(n, weight, std_err, alpha=0.05):\n",
163 |     "    t_value = stats.t.ppf(1 - alpha/2, df=n - 2)\n",
164 |     "    temp = t_value * std_err\n",
165 |     "    lower = weight - temp\n",
166 |     "    upper = weight + temp\n",
167 |     "\n",
168 |     "    return lower, upper"
169 |    ]
170 |   },
171 |   {
172 |    "cell_type": "code",
173 |    "execution_count": 6,
174 |    "metadata": {},
175 |    "outputs": [],
176 |    "source": [
177 |     "y_pred_proba = lor.predict_proba(X_std)\n",
178 |     "std_err = std_err_logisticregression(y_train, y_pred_proba, X_std)\n",
179 |     "\n",
180 |     "lower, upper = weight_intervals(len(y_train), lor.coef_.flatten(), std_err[1:])"
181 |    ]
182 |   },
183 |   {
184 |    "cell_type": "code",
185 |    "execution_count": 7,
186 |    "metadata": {},
187 |    "outputs": [
188 |     {
189 |      "data": {
190 |       "image/png": 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\n",
191 |       "text/plain": [
192 |        "<Figure size 432x288 with 1 Axes>"
193 |       ]
194 |      },
195 |      "metadata": {
196 |       "needs_background": "light"
197 |      },
198 |      "output_type": "display_data"
199 |     }
200 |    ],
201 |    "source": [
202 |     "fig, ax = plt.subplots()\n",
203 |     "\n",
204 |     "ax.hlines(0, xmin=-0.1, xmax=2.2, linestyle='dashed', color='skyblue')\n",
205 |     "ax.errorbar([0, 1, 2], lor.coef_.flatten(), yerr=upper - lor.coef_.flatten(), fmt='.k')\n",
206 |     "\n",
207 |     "ax.set_xticks([0, 1, 2])\n",
208 |     "ax.set_xticklabels([f'Sepal Length\\n({lor.coef_.flatten()[0]:.3f})',\n",
209 |     "                    f'Sepal Width\\n({lor.coef_.flatten()[1]:.3f})',\n",
210 |     "                    f'Petal Length\\n({lor.coef_.flatten()[2]:.3f})'])\n",
211 |     "plt.ylabel('Magnitude');"
212 |    ]
213 |   },
214 |   {
215 |    "cell_type": "code",
216 |    "execution_count": 8,
217 |    "metadata": {},
218 |    "outputs": [
219 |     {
220 |      "data": {
221 |       "text/plain": [
222 |        "(array([-4.77771221, -1.71641308,  4.43242151]),\n",
223 |        " array([-0.30375066,  1.2935979 , 17.16730293]))"
224 |       ]
225 |      },
226 |      "execution_count": 8,
227 |      "metadata": {},
228 |      "output_type": "execute_result"
229 |     }
230 |    ],
231 |    "source": [
232 |     "lower, upper"
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "markdown",
237 |    "metadata": {},
238 |    "source": [
239 |     "---"
240 |    ]
241 |   },
242 |   {
243 |    "cell_type": "code",
244 |    "execution_count": 9,
245 |    "metadata": {},
246 |    "outputs": [
247 |     {
248 |      "name": "stdout",
249 |      "output_type": "stream",
250 |      "text": [
251 |       "Optimization terminated successfully.\n",
252 |       "         Current function value: 0.120691\n",
253 |       "         Iterations: 10\n",
254 |       "         Function evaluations: 11\n",
255 |       "         Gradient evaluations: 20\n",
256 |       "         Hessian evaluations: 10\n"
257 |      ]
258 |     }
259 |    ],
260 |    "source": [
261 |     "import statsmodels.api as sm\n",
262 |     "\n",
263 |     "\n",
264 |     "model = sm.Logit(y_train, X_std)\n",
265 |     "res = model.fit(method='ncg')\n",
266 |     "lower, upper = res.conf_int(0.05)[:, 0], res.conf_int(0.05)[:, 1]"
267 |    ]
268 |   },
269 |   {
270 |    "cell_type": "code",
271 |    "execution_count": 10,
272 |    "metadata": {},
273 |    "outputs": [
274 |     {
275 |      "data": {
276 |       "text/plain": [
277 |        "(array([-4.78965224, -1.62105453,  4.48828742]),\n",
278 |        " array([-0.40139451,  1.25645007, 16.7429816 ]))"
279 |       ]
280 |      },
281 |      "execution_count": 10,
282 |      "metadata": {},
283 |      "output_type": "execute_result"
284 |     }
285 |    ],
286 |    "source": [
287 |     "lower, upper"
288 |    ]
289 |   },
290 |   {
291 |    "cell_type": "code",
292 |    "execution_count": 11,
293 |    "metadata": {},
294 |    "outputs": [
295 |     {
296 |      "data": {
297 |       "image/png": 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\n",
298 |       "text/plain": [
299 |        "<Figure size 432x288 with 1 Axes>"
300 |       ]
301 |      },
302 |      "metadata": {
303 |       "needs_background": "light"
304 |      },
305 |      "output_type": "display_data"
306 |     }
307 |    ],
308 |    "source": [
309 |     "fig, ax = plt.subplots()\n",
310 |     "\n",
311 |     "ax.hlines(0, xmin=-0.1, xmax=2.2, linestyle='dashed', color='skyblue')\n",
312 |     "ax.errorbar([0, 1, 2], res.params, yerr=upper - res.params, fmt='.k')\n",
313 |     "\n",
314 |     "ax.set_xticks([0, 1, 2])\n",
315 |     "ax.set_xticklabels([f'Sepal Length\\n({res.params[0]:.3f})',\n",
316 |     "                    f'Sepal Width\\n({res.params[1]:.3f})',\n",
317 |     "                    f'Petal Length\\n({res.params[2]:.3f})'])\n",
318 |     "plt.ylabel('Magnitude');"
319 |    ]
320 |   }
321 |  ],
322 |  "metadata": {
323 |   "kernelspec": {
324 |    "display_name": "Python 3",
325 |    "language": "python",
326 |    "name": "python3"
327 |   },
328 |   "language_info": {
329 |    "codemirror_mode": {
330 |     "name": "ipython",
331 |     "version": 3
332 |    },
333 |    "file_extension": ".py",
334 |    "mimetype": "text/x-python",
335 |    "name": "python",
336 |    "nbconvert_exporter": "python",
337 |    "pygments_lexer": "ipython3",
338 |    "version": "3.7.1"
339 |   }
340 |  },
341 |  "nbformat": 4,
342 |  "nbformat_minor": 4
343 | }
344 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2020 Sebastian Raschka
 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 | 


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/README.md:
--------------------------------------------------------------------------------
1 | # Interpretable Machine Learning Blog
2 | 
3 | Code examples for my Interpretable Machine Learning Blog Series
4 | 
5 | - Part 1: [https://sebastianraschka.com/blog/2020/interpretable-ml-1.html](https://sebastianraschka.com/blog/2020/interpretable-ml-1.html)


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