Travis CI: Add pytest --doctest-modules machine_learning (#1016)

* Travis CI: Add pytest --doctest-modules neural_network

Fixes #987
```
neural_network/perceptron.py:123: in <module>
    sample.insert(i, float(input('value: ')))
../lib/python3.7/site-packages/_pytest/capture.py:693: in read
    raise IOError("reading from stdin while output is captured")
E   OSError: reading from stdin while output is captured
-------------------------------------------------------------------------------- Captured stdout --------------------------------------------------------------------------------
('\nEpoch:\n', 399)
------------------------

value:
```

* Adding fix from #1056 -- thanks @QuantumNovice

* if __name__ == '__main__':

* pytest --ignore=virtualenv  # do not test our dependencies

machine_learning/perceptron.py

@@ -1,124 +0,0 @@
-'''
-
-	Perceptron
-	w = w + N * (d(k) - y) * x(k)
-
-	Using perceptron network for oil analysis,
-	with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2
-	p1 = -1
-	p2 = 1
-
-'''
-from __future__ import print_function
-
-import random
-
-
-class Perceptron:
-    def __init__(self, sample, exit, learn_rate=0.01, epoch_number=1000, bias=-1):
-        self.sample = sample
-        self.exit = exit
-        self.learn_rate = learn_rate
-        self.epoch_number = epoch_number
-        self.bias = bias
-        self.number_sample = len(sample)
-        self.col_sample = len(sample[0])
-        self.weight = []
-
-    def trannig(self):
-        for sample in self.sample:
-            sample.insert(0, self.bias)
-
-        for i in range(self.col_sample):
-           self.weight.append(random.random())
-
-        self.weight.insert(0, self.bias)
-
-        epoch_count = 0
-
-        while True:
-            erro = False
-            for i in range(self.number_sample):
-                u = 0
-                for j in range(self.col_sample + 1):
-                    u = u + self.weight[j] * self.sample[i][j]
-                y = self.sign(u)
-                if y != self.exit[i]:
-
-                    for j in range(self.col_sample + 1):
-
-                        self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j]
-                    erro = True
-            #print('Epoch: \n',epoch_count)
-            epoch_count = epoch_count + 1
-            # if you want controle the epoch or just by erro
-            if erro == False:
-                print(('\nEpoch:\n',epoch_count))
-                print('------------------------\n')
-            #if epoch_count > self.epoch_number or not erro:
-                break
-
-    def sort(self, sample):
-        sample.insert(0, self.bias)
-        u = 0
-        for i in range(self.col_sample + 1):
-            u = u + self.weight[i] * sample[i]
-
-        y = self.sign(u)
-
-        if  y == -1:
-            print(('Sample: ', sample))
-            print('classification: P1')
-        else:
-            print(('Sample: ', sample))
-            print('classification: P2')
-
-    def sign(self, u):
-        return 1 if u >= 0 else -1
-
-
-samples = [
-    [-0.6508, 0.1097, 4.0009],
-    [-1.4492, 0.8896, 4.4005],
-    [2.0850, 0.6876, 12.0710],
-    [0.2626, 1.1476, 7.7985],
-    [0.6418, 1.0234, 7.0427],
-    [0.2569, 0.6730, 8.3265],
-    [1.1155, 0.6043, 7.4446],
-    [0.0914, 0.3399, 7.0677],
-    [0.0121, 0.5256, 4.6316],
-    [-0.0429, 0.4660, 5.4323],
-    [0.4340, 0.6870, 8.2287],
-    [0.2735, 1.0287, 7.1934],
-    [0.4839, 0.4851, 7.4850],
-    [0.4089, -0.1267, 5.5019],
-    [1.4391, 0.1614, 8.5843],
-    [-0.9115, -0.1973, 2.1962],
-    [0.3654, 1.0475, 7.4858],
-    [0.2144, 0.7515, 7.1699],
-    [0.2013, 1.0014, 6.5489],
-    [0.6483, 0.2183, 5.8991],
-    [-0.1147, 0.2242, 7.2435],
-    [-0.7970, 0.8795, 3.8762],
-    [-1.0625, 0.6366, 2.4707],
-    [0.5307, 0.1285, 5.6883],
-    [-1.2200, 0.7777, 1.7252],
-    [0.3957, 0.1076, 5.6623],
-    [-0.1013, 0.5989, 7.1812],
-    [2.4482, 0.9455, 11.2095],
-    [2.0149, 0.6192, 10.9263],
-    [0.2012, 0.2611, 5.4631]
-
-]
-
-exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]
-
-network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
-
-network.trannig()
-
-while True:
-    sample = []
-    for i in range(3):
-        sample.insert(i, float(input('value: ')))
-    network.sort(sample)

machine_learning/random_forest_classification/random_forest_classification.py

@@ -1,17 +1,19 @@
 # Random Forest Classification
 
 # Importing the libraries
+import os
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
 # Importing the dataset
-dataset = pd.read_csv('Social_Network_Ads.csv')
+script_dir = os.path.dirname(os.path.realpath(__file__))
+dataset = pd.read_csv(os.path.join(script_dir, 'Social_Network_Ads.csv'))
 X = dataset.iloc[:, [2, 3]].values
 y = dataset.iloc[:, 4].values
 
 # Splitting the dataset into the Training set and Test set
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 
 # Feature Scaling

machine_learning/random_forest_regression/random_forest_regression.py

@@ -1,12 +1,14 @@
 # Random Forest Regression
 
 # Importing the libraries
+import os
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
 # Importing the dataset
-dataset = pd.read_csv('Position_Salaries.csv')
+script_dir = os.path.dirname(os.path.realpath(__file__))
+dataset = pd.read_csv(os.path.join(script_dir, 'Position_Salaries.csv'))
 X = dataset.iloc[:, 1:2].values
 y = dataset.iloc[:, 2].values
 
@@ -28,7 +30,7 @@ regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)
 regressor.fit(X, y)
 
 # Predicting a new result
-y_pred = regressor.predict(6.5)
+y_pred = regressor.predict([[6.5]])
 
 # Visualising the Random Forest Regression results (higher resolution)
 X_grid = np.arange(min(X), max(X), 0.01)

neural_network/perceptron.py

@@ -113,13 +113,13 @@ samples = [
 
 exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]
 
+network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
+
+network.training()
+
 if __name__ == '__main__':
-    network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
-
-    network.training()
-
     while True:
         sample = []
         for i in range(3):
-            sample.insert(i, float(input('value: ').strip()))
+            sample.insert(i, float(input('value: ')))
         network.sort(sample)

requirements.txt

@@ -7,6 +7,7 @@ opencv-python
 pandas
 pillow
 pytest
+requests
 sklearn
 sympy
 tensorflow