def evaluate(self, data_frame, array_datas):
"""
Run Isomap on a dataset of multiple arrays
"""
# Get the data frame
if data_frame is not None:
df = data_frame.data
df = DataGuru.convertStrCols_toNumeric(df)
cname = data_frame.name
elif array_datas is not None:
command_status, df, kl1, cname = DataGuru.transformArray_to_dataFrame(
array_datas, useCategorical=True)
if command_status == CommandStatus.Error:
return ResultObject(None, None, None, CommandStatus.Error)
else:
Printer.Print("Please provide data frame or arrays to analyze")
return ResultObject(None, None, None, CommandStatus.Error)
Y = None
if StatContainer.ground_truth is not None:
df = DataGuru.removeGT(df, StatContainer.ground_truth)
Y = StatContainer.filterGroundTruth()
df, Y = DataGuru.removenan(df, Y)
# Remove nans:
else:
df.dropna(inplace=True)
# Get the Isomap model
# Code to run the classifier
X = df.values
# Get a standard scaler for the extracted data X
scaler = preprocessing.StandardScaler().fit(X)
X = scaler.transform(X)
# Train the classifier
win = Window.window()
properties = self.createDefaultProperties()
properties['title'] = cname
# return ResultObject(None, None, None, CommandStatus.Success)
if data_frame is not None:
result_object = VizContainer.createResult(win, data_frame,
['ismp'])
else:
result_object = VizContainer.createResult(win, array_datas,
['ismp'])
result_object.data = [win, properties, [X, Y], self.updateFigure]
self.updateFigure(result_object.data)
self.modify_figure.evaluate(result_object)
return result_object
def evaluate(self, data_frame, array_datas):
"""
Run pca on a dataset of multiple arrays
"""
# Get the data frame
if data_frame is not None:
df = data_frame.data
df = DataGuru.convertStrCols_toNumeric(df)
cname = data_frame.name
elif array_datas is not None:
command_status, df, kl1, cname = DataGuru.transformArray_to_dataFrame(
array_datas, useCategorical=True)
if command_status == CommandStatus.Error:
return ResultObject(None, None, None, CommandStatus.Error)
else:
Printer.Print("Please provide data frame or arrays to analyze")
return ResultObject(None, None, None, CommandStatus.Error)
Y = None
if StatContainer.ground_truth is not None:
df = DataGuru.removeGT(df, StatContainer.ground_truth)
Y = StatContainer.filterGroundTruth()
# Remove nans:
df, Y = DataGuru.removenan(df, Y)
else:
df.dropna(inplace=True)
# Code to run the classifier
X = df.values
# Get a standard scaler for the extracted data X
scaler = preprocessing.StandardScaler().fit(X)
X = scaler.transform(X)
# Train the classifier
pca = PCA(n_components=2)
pca_res = pca.fit_transform(X)
win = Window.window()
f = win.gcf()
ax = f.add_subplot(111)
if Y is None:
sc = ax.scatter(pca_res[:, 0],
pca_res[:, 1],
cmap="jet",
edgecolor="None",
alpha=0.35)
else:
sc = ax.scatter(pca_res[:, 0],
pca_res[:, 1],
c=Y,
cmap="jet",
edgecolor="None",
alpha=0.35)
cbar = plt.colorbar(sc)
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel(StatContainer.ground_truth.name, rotation=270)
ax.set_title(cname)
win.show()
# return ResultObject(None, None, None, CommandStatus.Success)
if data_frame is not None:
return VizContainer.createResult(win, data_frame, ['pca'])
else:
return VizContainer.createResult(win, array_datas, ['pca'])
def evaluate(self, data_frame, array_datas, target):
"""
Run clustering on a dataset of multiple arrays
"""
# Get the data frame
if data_frame is not None:
df = data_frame.data
cname = data_frame.name
elif array_datas is not None:
command_status, df, kl1, cname = DataGuru.transformArray_to_dataFrame(
array_datas, useCategorical=True)
if command_status == CommandStatus.Error:
return ResultObject(None, None, None, CommandStatus.Error)
else:
Printer.Print("Please provide data frame or arrays to analyze")
return ResultObject(None, None, None, CommandStatus.Error)
Y = None
if StatContainer.ground_truth is not None:
df = DataGuru.removeGT(df, StatContainer.ground_truth)
Y = StatContainer.filterGroundTruth()
# Remove nans:
df, Y = DataGuru.removenan(df, Y)
else:
df.dropna(inplace=True)
# Get the tsne model
# Code to run the classifier
X = df.values
# Get a standard scaler for the extracted data X
scaler = preprocessing.StandardScaler().fit(X)
X = scaler.transform(X)
# Train the classifier
numbers = findNumbers(target.data, 1)
if numbers != [] and numbers[0].data > 0:
num_clusters = int(numbers[0].data)
else:
num_clusters = 2 # If not specified use 2 clusters
kY = self.performOperation(X, num_clusters)
result_objects = []
if StatContainer.ground_truth is not None:
df_res = pd.DataFrame()
df_res['ground_truth'] = Y
df_res['clustering_result'] = kY
df_res.pivot_table(index=df_res.columns[0],
columns=df_res.columns[1],
aggfunc=np.size,
fill_value=0)
win = Window.window()
f = win.gcf()
ax = f.add_subplot(111)
df_res = DataGuru.convertStrCols_toNumeric(df_res)
sns.heatmap(df_res, ax=ax)
win.show()
if data_frame is not None:
result_object = VizContainer.createResult(
win, data_frame, ['clstr.fig'])
else:
result_object = VizContainer.createResult(
win, array_datas, ['clstr.fig'])
result_objects.append(result_object)
result_object = ResultObject(kY, [], DataType.array,
CommandStatus.Success)
result_object.createName(cname,
command_name="clstr",
set_keyword_list=True)
result_objects.append(result_object)
return result_objects
