cache

code/.convert_notebook_to_script.py

@@ -7,7 +7,6 @@
 import argparse
 import os
 import subprocess
-import textwrap
 
 
 def convert(input_path, output_path):
@@ -26,15 +25,23 @@ def cleanup(path):
 
     clean_content = []
     imports = []
+    existing_imports = set()
     with open(path, 'r') as f:
         next(f)
         next(f)
         for line in f:
+            line = line.rstrip(' ')
             if line.startswith(skip_lines_startwith):
                 continue
-            if line.startswith('import') or (
-                    'from' in line and 'import' in line):
-                imports.append(line)
+            if line.startswith('import ') or (
+                    'from ' in line and 'import ' in line):
+                if 'from __future__ import print_function' in line:
+                    if line != imports[0]:
+                        imports.insert(0, line)
+                else:
+                    if line.strip() not in existing_imports:
+                        imports.append(line)
+                        existing_imports.add(line.strip())
             else:
                 clean_content.append(line)
 

code/ch02/ch02.ipynb

@@ -1363,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {

code/ch02/ch02.py

@@ -4,7 +4,6 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-import numpy as np
 from matplotlib.colors import ListedColormap
 
 # *Python Machine Learning 2nd Edition* by [Sebastian Raschka](https://sebastianraschka.com), Packt Publishing Ltd. 2017

code/ch03/ch03.ipynb

@@ -1743,7 +1743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -1758,6 +1758,15 @@
    "source": [
     "! python ../.convert_notebook_to_script.py --input ch03.ipynb --output ch03.py"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

code/ch03/ch03.py

@@ -11,16 +11,9 @@
 from sklearn.metrics import accuracy_score
 from matplotlib.colors import ListedColormap
 import matplotlib.pyplot as plt
-import matplotlib.pyplot as plt
-import numpy as np
 from sklearn.linear_model import LogisticRegression
 from sklearn.svm import SVC
 from sklearn.linear_model import SGDClassifier
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.svm import SVC
-import matplotlib.pyplot as plt
-import numpy as np
 from sklearn.tree import DecisionTreeClassifier
 from pydotplus import graph_from_dot_data
 from sklearn.tree import export_graphviz

code/ch04/ch04.ipynb

@@ -2522,7 +2522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {

code/ch04/ch04.py

@@ -5,25 +5,19 @@
 from io import StringIO
 import sys
 from sklearn.preprocessing import Imputer
-import pandas as pd
 import numpy as np
 from sklearn.preprocessing import LabelEncoder
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression
-from sklearn.linear_model import LogisticRegression
 import matplotlib.pyplot as plt
 from sklearn.base import clone
 from itertools import combinations
-import numpy as np
 from sklearn.metrics import accuracy_score
-from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.ensemble import RandomForestClassifier
-importances = forest.feature_importances_
 from sklearn.feature_selection import SelectFromModel
 
 # *Python Machine Learning 2nd Edition* by [Sebastian Raschka](https://sebastianraschka.com), Packt Publishing Ltd. 2017
@@ -592,6 +586,7 @@ def _calc_score(self, X_train, y_train, X_test, y_test, indices):
                                 random_state=1)
 
 forest.fit(X_train, y_train)
+importances = forest.feature_importances_
 
 indices = np.argsort(importances)[::-1]
 

code/ch05/ch05.ipynb

@@ -2125,7 +2125,7 @@
      "output_type": "stream",
      "text": [
       "[NbConvertApp] Converting notebook ch05.ipynb to script\n",
-      "[NbConvertApp] Writing 27719 bytes to ch05.py\n"
+      "[NbConvertApp] Writing 27705 bytes to ch05.py\n"
      ]
     }
    ],

code/ch05/ch05.py

@@ -10,19 +10,11 @@
 from matplotlib.colors import ListedColormap
 from sklearn.linear_model import LogisticRegression
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
-from sklearn.linear_model import LogisticRegression
 from scipy.spatial.distance import pdist, squareform
 from scipy import exp
 from scipy.linalg import eigh
-import numpy as np
-import matplotlib.pyplot as plt
 from sklearn.datasets import make_moons
-from sklearn.decomposition import PCA
 from sklearn.datasets import make_circles
-from scipy.spatial.distance import pdist, squareform
-from scipy import exp
-from scipy.linalg import eigh
-import numpy as np
 from sklearn.decomposition import KernelPCA
 
 # *Python Machine Learning 2nd Edition* by [Sebastian Raschka](https://sebastianraschka.com), Packt Publishing Ltd. 2017
@@ -922,8 +914,3 @@ def project_x(x_new, X, gamma, alphas, lambdas):
 
 
 
-
-
-
-
-