def select_k_best(self, feature, k=5):
"""
:param feature: str
Name of feature (column name) to predict
:param k: int
Number of features to select
:return: void
Selects the best features to predict feature
"""
print("Selecting k best features of data file", self.dataset_file)
headers, raw_data = self.parse_csv() # get columns names and raw data
sel = feature_selection.SelectKBest(feature_selection.f_regression,
k=k) # model to select data
x_matrix_features = self.HEADERS_TO_ANALYZE.copy() # not edit main list of headers
x_matrix_features.remove(
feature) # do NOT include feature to predict in input matrix
x_data = m_utils.get_subset_of_matrix(x_matrix_features, headers,
raw_data) # input matrix
y_data = m_utils.get_subset_of_matrix([feature], headers,
raw_data) # output matrix
sel.fit(x_data, y_data) # fit
top_k_features_indices = np.array(sel.scores_).argsort()[-k:][
::-1] # indices of top k features
top_k_features = [x_matrix_features[i] for i in
top_k_features_indices] # names of top k features
top_k_features_scores = [sel.scores_[i] for i in
top_k_features_indices] # scores of top k features
chart = create_symlog_bar_chart(
"Most " + str(k) + " correlated features with " + feature,
top_k_features,
top_k_features_scores, "score")
plt.show()
def predict_feature(self, feature):
"""
:param feature: str
Name of feature (column name) to predict
:return: void
Predicts feature with linear regression
"""
print("Predicting ", feature, "with data from file", self.dataset_file)
headers, raw_data = self.parse_csv() # get columns names and raw data
clf = linear_model.LinearRegression() # model to fit data
x_matrix_features = self.HEADERS_TO_ANALYZE.copy()
x_matrix_features.remove(
feature) # do NOT include feature to predict in input matrix
x_data = m_utils.get_subset_of_matrix(x_matrix_features, headers,
raw_data) # input matrix
y_data = m_utils.get_subset_of_matrix([feature], headers,
raw_data) # output matrix
clf.fit(x_data, y_data)
coefficients = {} # dict feature -> coefficient
for i in range(len(x_matrix_features)):
coefficients[x_matrix_features[i]] = clf.coef_[0][i]
chart = create_symlog_bar_chart(
"Linear fit of " + feature,
[k for k in coefficients.keys()],
coefficients.values(),
"Coefficient"
)
plt.show()
def cluster_3d_plot(self, labels, n_clusters=6):
"""
:param labels: [] of str (len = 3)
Features to cluster data. Each item must be in the csv data file. Each label is one of x, y, z axis
:param n_clusters: int
Number of clusters
:return: void
Plots 3D chart with clusters based on selected features
"""
print("Clustering file", self.dataset_file)
headers, raw_data = self.parse_csv() # get columns names and raw data
x_data = m_utils.get_subset_of_matrix(self.HEADERS_TO_ANALYZE, headers,
raw_data) # input matrix
kmeans = cluster.KMeans(n_clusters=n_clusters, random_state=0).fit(
x_data)
fig = plt.figure(figsize=(4, 3)) # create 3D plot
ax = fig.add_subplot(111, projection="3d")
ax.scatter(
x_data[:, self.HEADERS_TO_ANALYZE.index(labels[0])],
# get values of given labels
x_data[:, self.HEADERS_TO_ANALYZE.index(labels[1])],
x_data[:, self.HEADERS_TO_ANALYZE.index(labels[2])],
c=kmeans.labels_.astype(np.float)
) # plot 3D data points
centroids = kmeans.cluster_centers_
cluster_centers = [] # list of centers of each cluster
for i in range(n_clusters):
cl_center = {
"x": centroids[i][self.HEADERS_TO_ANALYZE.index(labels[0])],
# x-coordinate of i-th cluster
"y": centroids[i][self.HEADERS_TO_ANALYZE.index(labels[1])],
# y-coordinate of i-th cluster
"z": centroids[i][self.HEADERS_TO_ANALYZE.index(labels[2])]
# z-coordinate of i-th cluster
} # x, y, z of center of first cluster -> find x, y, z of each label
cluster_centers.append(cl_center)
ax.scatter(
[c["x"] for c in cluster_centers],
# x positions of centers of all clusters
[c["y"] for c in cluster_centers],
# y positions of centers of all clusters
[c["z"] for c in cluster_centers],
# z positions of centers of all clusters
marker='o',
s=800,
linewidth=5,
color='w'
) # plot centroids
ax.set_xlabel(labels[0]) # set labels
ax.set_ylabel(labels[1])
ax.set_zlabel(labels[2])
plt.title(str(n_clusters) + "-clustering data")
plt.show()
def cluster_analyze(self, n_clusters=6):
"""
:param n_clusters: int
Number of clusters
:return: void
Computes cluster analysis: see days based on differences.
Each day is different from one another, there are days where you trained more, others where you ate more ...
The goal is to divide your days into categories (e.g highly-active, active ...) based on data logs.
This way, the input matrix consists of multiple vectors with each one consisting of one day's values.
"""
print("Clustering file", self.dataset_file)
headers, raw_data = self.parse_csv() # get columns names and raw data
x_data = m_utils.get_subset_of_matrix(self.HEADERS_TO_ANALYZE, headers,
raw_data) # input matrix
kmeans = cluster.KMeans(n_clusters=n_clusters, random_state=0).fit(
x_data)
print("Clusters", kmeans.labels_)
headers_to_plot = [
"SUMMARY:kcal_count",
"STEPS:distance",
"SLEEP:deep_sleep_time",
"ACTIVITIES:distance"
] # get headers to add to chart
vals_headers = [
[float(row[headers.index(h)]) for row in raw_data] for h in
headers_to_plot
] # get values for each header
headers_to_plot.append("cluster") # add cluster group
vals_headers.append(kmeans.labels_)
days = [str(row[headers.index("date")]) for row in
raw_data] # get list of days (x values)
chart = create_multiple_bar_chart(
"Days",
days,
vals_headers,
headers_to_plot
) # create chart
plt.show() # show bar chart
