def test_on_animal_df(self):
"""
Simple example with expected numpy vector to compare to.
Use fillna mode.
"""
animal_df = pd.DataFrame({'animal': ['dog', 'cat', 'rat'],
'color': ['white', 'brown', 'brown'],
'gender': ['F', 'F', np.NaN],
'weight': [25, 5, 1],
'garbage': [0, 1, np.NaN],
'abundance': [0.5, 0.4, 0.1]})
extracted = bacteriopop_utils.extract_features(
dataframe=animal_df,
column_list=['animal', 'color', 'weight', 'abundance'],
fillna=True
)
# check that the column names match what is expected
self.assertEqual(extracted.columns.tolist(),
['abundance', 'animal=cat', 'animal=dog',
'animal=rat', 'color=brown', 'color=white',
'weight'])
# check that the values are what was expected.
expected_result = np.array([[0.5, 0., 1., 0., 0., 1., 25.],
[0.4, 1., 0., 0., 1., 0., 5.],
[0.1, 0., 0., 1., 1., 0., 1.]])
self.assertEqual(expected_result.tolist(),
extracted.as_matrix().tolist())
def dbscan_demo():
print 'starting up'
df = load_data()
print 'load done'
df = extract_features(df)
print 'features done'
dbscan(df, 0.2, 10)
def dbscan_demo():
print 'starting up'
df = load_data()
print 'load done'
df = extract_features(df)
print 'features done'
dbscan(df, 0.2, 10)
def gmm_demo():
print 'starting up'
df = load_data()
print 'load done'
df = extract_features(df)
print 'extract done'
features_list = list(df.columns.values)[1:]
print 'features done'
gmm(df, features_list)
def gmm_demo():
print 'starting up'
df = load_data()
print 'load done'
df = extract_features(df)
print 'extract done'
features_list = list(df.columns.values)[1:]
print 'features done'
gmm(df, features_list)
def main():
"""
Entry point for all code
"""
print "starting up"
df = load_data()
df_vectorized = extract_features(df, column_list=FEATURES_TO_EXTRACT,
fillna=True, debug=False)
target_correlation = calculate_features_target_correlation(df_vectorized, df_vectorized.columns.tolist(),
PREDICTION_TARGET, PCA_METHOD)
pca = pca_bacteria(df_vectorized, PCA_COMPONENTS)
return target_correlation, pca
import random
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import DBSCAN
from load_data import load_data
from bacteriopop_utils import extract_features
DF = load_data()
DF = extract_features(DF)
def get_random_color(pastel_factor=0.5):
return [(x+pastel_factor)/(1.0+pastel_factor) for x in
[random.uniform(0, 1.0) for i in [1, 2, 3]]]
def color_distance(c1, c2):
return sum([abs(x[0]-x[1]) for x in zip(c1, c2)])
def generate_new_color(existing_colors, pastel_factor=0.5):
max_distance = None
best_color = None
for i in range(0, 100):
color = get_random_color(pastel_factor=pastel_factor)
if not existing_colors:
return color
best_distance = min([color_distance(color, c) for c in
existing_colors])
if not max_distance or best_distance > max_distance:
max_distance = best_distance
import random
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import DBSCAN
from load_data import load_data
from bacteriopop_utils import extract_features
DF = load_data()
DF = extract_features(DF)
def get_random_color(pastel_factor=0.5):
return [(x + pastel_factor) / (1.0 + pastel_factor)
for x in [random.uniform(0, 1.0) for i in [1, 2, 3]]]
def color_distance(c1, c2):
return sum([abs(x[0] - x[1]) for x in zip(c1, c2)])
def generate_new_color(existing_colors, pastel_factor=0.5):
max_distance = None
best_color = None
for i in range(0, 100):
color = get_random_color(pastel_factor=pastel_factor)
if not existing_colors:
return color
best_distance = min(
[color_distance(color, c) for c in existing_colors])
if not max_distance or best_distance > max_distance:
def gmm_demo():
df = load_data()
df = extract_features(df)
features_list = list(df.columns.values)[1:]
gmm(df, features_list)
def gmm_demo():
df = load_data()
df = extract_features(df)
features_list = list(df.columns.values)[1:]
gmm(df, features_list)
