def elbow_plot_handler_old(request):
resp_data = dict()
file_name = request.GET.get("file_name")
column_header = request.GET.get("column_header")
exclude_columns = request.GET.get("exclude_columns")
print(column_header)
if file_name:
fs = FileStorage()
file_full_path = fs.get_base_location() + file_name
# If the file does exist, read data by panda and drop columns (if any)
if fs.is_file(file_full_path):
# Get data from file
column_header_idx = None
if column_header == "on":
column_header_idx = 0;
df = DataFrameUtil.convert_file_to_dataframe(file_full_path, header=column_header_idx)
# Drop column specified by user
if exclude_columns:
str_column_indexs = exclude_columns.split(",")
# column_indexs = list(map(int, str_column_indexs))
column_indexs = [int(i) - 1 for i in str_column_indexs]
df = DataFrameUtil.drop_column_by_index(df, column_indexs)
is_nan = np.any(np.isnan(df))
is_finite = np.all(np.isfinite(df))
# Standardize data
X_scaled = PreProcessingUtil.standardize(df)
# Get explain variance ratio
pca_helper = PcaUtil()
pca = pca_helper.get_fit_transfrom_pca(X_scaled)
arr_variance_ratio = pca.explained_variance_ratio_
# Prepare all tabs to display Plot, Table by Bokeh
# Add ratio to bokeh line graph
elbow_plot = draw_elbow_plot(arr_variance_ratio)
# Describe data
# df_describe = df.describe().to_json()
# df_describe_table = draw_df_describe_table(df)
# Add line to a panel
tab1 = Panel(child=elbow_plot, title="Elbow Curve Plot")
# tab2 = Panel(child=df_describe_table, title="Data Description")
# Add a panel to tab
tabs = Tabs(tabs=[ tab1 ])
script, div = components(tabs)
plots = { 'script': script, 'div': div}
resp_data["bokeh_plot"] = plots
# resp_data["data_describe"] = bokeh_df_describe_table
else:
resp_data["msg"] = "[ERROR] File is not found."
else:
resp_data['msg'] = "[ERROR] File name is invalid."
return JsonResponse(resp_data)
def dataframe_exclude_columns(df, exclude_columns):
"""
exclude_columns - A string array of column entered by user from 1, 2, ...
"""
if exclude_columns:
str_column_indexs = exclude_columns.split(",")
column_indexs = [int(i) - 1 for i in str_column_indexs]
return DataFrameUtil.drop_column_by_index(df, column_indexs)