mohcinemadkour

mohcinemadkour

A Typical directory structure for running tests using unittest

Ref : stackoverflow

The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).

For example for a directory structure like this:

new_project

├── antigravity.py

mohcinemadkour

A Typical directory structure for running tests using unittest

Ref : stackoverflow

The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).

For example for a directory structure like this:

new_project

├── antigravity.py

mohcinemadkour

for i in range(len(imfs1)):
  fig, ax = plt.subplots(figsize=(25,1))
  fig = plt.plot(imfs1[i], label = "IMF{}".format(i))
  plt.legend()
  plt.show()

mohcinemadkour

App Insights: 8de2bc6e-8885-43c4-892d-ba8440bb6026

mohcinemadkour

'''
Author : Mohcine Madkour
Email : [email protected]
Date : 10-Sep-2014
Description: Simulations of Schelling's seggregation model
http://www.binpress.com/tutorial/introduction-to-agentbased-models-an-implementation-of-schelling-model-in-python/144
'''

import matplotlib.pyplot as plt
import itertools

mohcinemadkour

'''
Author : Mohcine Madkour
Email : [email protected]
Date : 10-Sep-2014
Description: Simulations of Schelling's seggregation model
http://www.binpress.com/tutorial/introduction-to-agentbased-models-an-implementation-of-schelling-model-in-python/144
'''

import matplotlib.pyplot as plt
import itertools

mohcinemadkour

# prepare for modeling
X_train = train.drop(['id', 'target'], axis=1)
y_train = train['target']

X_test = test.drop(['id'], axis=1)

# scaling data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

mohcinemadkour

penalty = ['l1', 'l2']
C = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]
class_weight = [{1:0.5, 0:0.5}, {1:0.4, 0:0.6}, {1:0.6, 0:0.4}, {1:0.7, 0:0.3}]
solver = ['liblinear', 'saga']

param_grid = dict(penalty=penalty,
                  C=C,
                  class_weight=class_weight,
                  solver=solver)

mohcinemadkour

loss = ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']
penalty = ['l1', 'l2', 'elasticnet']
alpha = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]
learning_rate = ['constant', 'optimal', 'invscaling', 'adaptive']
class_weight = [{1:0.5, 0:0.5}, {1:0.4, 0:0.6}, {1:0.6, 0:0.4}, {1:0.7, 0:0.3}]
eta0 = [1, 10, 100]

param_distributions = dict(loss=loss,
                           penalty=penalty,
                           alpha=alpha,