def rc_compare(n_clicks):
key = db.get("rc.model")
models = db.get('models')
if key is None:
return common.error_msg("Select Model for Details!!")
if models is None:
return common.error_msg("No Model has been trainned, Please Train Models First!!")
return get_model_div(key)
def rc_compare(n_clicks):
file = db.get("rc.file")
models = db.get('models')
if file is None:
return common.error_msg("Select Models for Comparison!!")
if models is None:
return common.error_msg(
"No Model has been trainned, Please Train Models First!!")
compare_div = get_compare_div(file)
return html.Div([compare_div, html.Br(), html.Hr()])
def get_compare_div(file):
models = db.get('models')
keys = []
for key, value in models.items():
f = value['file']
if f == file:
keys.append(key)
if len(keys) == 0:
return common.error_msg("No Model has been trainned for " + file +
". Please Train Models First!!")
tags = []
for tag in get_property(keys, 'tag'):
tags.append('Tag ' + str(tag))
df = pd.DataFrame(columns=['Model Name'] + keys)
df.loc[0] = ['Tag :'] + tags
df.loc[1] = ['Type :'] + get_property(keys, 'type')
df.loc[2] = ['No of Coefficients :'] + get_property(keys, 'params')
df.loc[3] = ['F Statistics :'] + get_property(keys, 'anova', 'F')
df.loc[4] = ['Cofficient of Determination :'] + get_property(
keys, 'anova', 'R2')
df.loc[5] = ['Error Mean :'] + get_property(keys, 'error_mean')
df.round(4)
div = html.Div([
dbc.Table.from_dataframe(df,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
])
return div
def lr_predict_data(n_clicks):
predict_data = db.get('cl.predict_data')
if predict_data is None:
return ""
predict_data = get_predict_data_list(predict_data)
model = db.get("lr.model")
params = db.get("lr.params")
if len(predict_data) != len(params) - 1:
return common.error_msg('Pass Valid InDependent Variables!!')
predicted = model.predict(predict_data)
return common.success_msg('Predicted Dependent Variable = ' +
str(predicted))
def clean_save_file(n):
## Team 2 API Integration
df = db.get("raw_data")
file = db.get("file")
sheet = db.get("sheet")
tags = db.get('tags')
div = None
if (not n is None) and (not df is None):
try:
df, cleaned_df, defective_df, stats = data_cleaning(df)
if not sheet is None:
file = FileUtils.append_file_name(file, sheet)
file = file.split('.')[0]
path = FileUtils.path('clean', file)
cleaned_df.to_csv(path, index=False)
### Tag the cleaned data ###
if file in tags:
tags[file] = tags[file] + 1
else:
tags[file] = 1
col_df = pd.DataFrame(columns=stats['col_name'])
col_df.loc[0] = stats['col_type']
stat_df = pd.DataFrame(columns=[
'Tag', 'Total no of Records', 'Cleaned no of Records',
'Defective no of Records'
])
stat_df.loc[0] = [
'Tag ' + str(tags[file]), stats['row_total'],
stats['row_cleaned'], stats['row_defect']
]
div = html.Div([
common.success_msg("File is Cleaned & Saved Successfully!!"),
html.H2('Cleaned Data Statistic'),
dbc.Table.from_dataframe(stat_df,
striped=True,
bordered=True,
hover=True,
style=common.table_style),
html.H2('Cleaned Data Schema'),
dbc.Table.from_dataframe(col_df,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
],
style={'margin': '10px'})
except Exception as e:
return common.error_msg("Data Cleansing API Error: " + str(e))
return div
def apply_file_properties(n):
file = db.get("file")
format = db.get("format")
sep = db.get("file_separator")
header = db.get("file_header")
div = None
df = None
if format is None:
div = None
return div
elif (format == 'csv' or format == 'txt' or format == 'xls'
or format == 'xlsx') and header is None:
div = common.error_msg('Please Select Header!!')
return div
elif format == 'csv' or format == 'txt':
if sep is None:
sep = ','
db.put("file_separator", sep)
path = FileUtils.path('raw', file)
df = DataUtils.read_csv(path, sep, header)
msg = "Following Properties Applied. Separator=" + sep + " Header=" + str(
header)
elif format == 'xls' or format == 'xlsx':
path = FileUtils.path('raw', file)
sheet = db.get("sheet")
df = DataUtils.read_xls(path, sheet, header)
msg = "Following Properties Applied. Header=" + str(header)
table = dbc.Table.from_dataframe(df.head(10),
striped=True,
bordered=True,
hover=True,
style=common.table_style)
button = dbc.Button("Clean & Save", color="primary", id='clean-save-file')
div = [
common.msg(msg), table,
html.Div(
[button, html.Br(),
html.Div([], id="cleaned-saved-file")],
style={
'padding': '10px',
'textAlign': 'center'
})
]
db.put("raw_data", df)
return div
def stats_table_and_linear_regression(json_ordered_data):
if json_ordered_data is None:
return (common.msg(None), generate_table(pd.DataFrame(columns=[])),
generate_table(pd.DataFrame(columns=[])), y_ycap_fig,
error_fig, "", "")
dff = pd.read_json(json_ordered_data, orient='split')
col = list(dff.columns)
y = list(dff[col[-1]])
data = []
x_col = col[:-1]
y_col = col[-1]
data = [[] for i in range(len(x_col))]
for i in range(len(x_col)):
x = dff[x_col[i]].values.tolist()
data[i] = x
##Team 3 API Integration
try:
model = LinearRegression()
db.put("lr.model", model)
db.put("lr.x_col", x_col)
db.put("lr.y_col", y_col)
(summary, params, ycap) = model.fit(data, y)
db.put("lr.summary", summary)
db.put("lr.params", params)
db.put("lr.ycap", ycap)
error_mean = model.model_stats()['mean']
db.put("lr.error_mean", error_mean)
except (Exception, ValueError) as e:
return (common.error_msg("Linear Regression API Error: " + str(e)),
generate_table(pd.DataFrame(columns=[])),
generate_table(pd.DataFrame(columns=[])), y_ycap_fig,
error_fig, "", "")
df_stats = common.get_stats_df(summary, x_col, y_col)
table1 = dbc.Table.from_dataframe(df_stats,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
df_coeff = common.get_coeff_df(params, x_col)
table2 = dbc.Table.from_dataframe(df_coeff,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
trace_1 = go.Scatter(x=list(range(len(y))),
y=ycap,
name='Y Predicted (ŷ)',
line=dict(width=2, color='rgb(229, 151, 50)'))
trace_2 = go.Scatter(x=list(range(len(y))),
y=y,
name='Y Actual',
line=dict(width=2, color='rgb(106, 181, 135)'))
ydiff = [y[i] - ycap[i] for i in range(len(y))]
trace_3 = go.Scatter(x=list(range(len(y))),
y=ydiff,
line=dict(width=2, color='rgb(236, 10, 15)'))
fig1 = go.Figure(data=[trace_1, trace_2], layout=y_ycap_title)
fig2 = go.Figure(data=[trace_3], layout=error_title)
error_mean = html.H2('Error Mean = ' +
str(round(db.get('lr.error_mean'), 4)))
##Team 5 API Integration
anova = get_anova(y, ycap, len(params))
db.put('lr.anova', anova)
anova_div = common.get_anova_div(anova)
return (
common.success_msg("Linear Regression API Exceuted Successfully!!"),
table1, table2, fig1, fig2, error_mean, anova_div)
def stats_table_and_hor_regression(json_ordered_data, hor_order):
if json_ordered_data is None or hor_order is None:
return (common.msg(None), generate_table(pd.DataFrame(columns=[])),
generate_table(pd.DataFrame(columns=[])), y_ycap_fig,
error_fig, "", "", "")
dff = pd.read_json(json_ordered_data, orient='split')
col = list(dff.columns)
x_col = [col[0]]
y_col = col[1]
dff = dff.sort_values(by=x_col)
x = list(dff[col[0]])
y = list(dff[col[1]])
##Team 4 API Integration
try:
db.put("hor.x_col", x_col)
db.put("hor.y_col", y_col)
(ycap, params) = Building_model_equation(x, y, hor_order)
print(params)
params = params.tolist()
print(params)
db.put("hor.params", params)
db.put("hor.ycap", ycap)
db.put("hor.order", hor_order)
error = 10.0
for i in range(len(y)):
error += y[i] - ycap[i]
error_mean = error / len(y)
db.put("hor.error_mean", error_mean)
db.put("hor.error_mean", error_mean)
## Team 3 API call for Summary Statistics
model = LinearRegression()
(summary, params_ignore, ycap_ignore) = model.fit([x], y)
db.put("hor.summary", summary)
except (Exception, ValueError) as e:
return (common.error_msg("Higher Order Regression API Error: " +
str(e)),
generate_table(pd.DataFrame(columns=[])),
generate_table(pd.DataFrame(columns=[])), y_ycap_fig,
error_fig, "", "", "")
df_stats = common.get_stats_df(summary, x_col, y_col)
table1 = dbc.Table.from_dataframe(df_stats,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
df_coeff = common.hor_get_coeff_df(params)
table2 = dbc.Table.from_dataframe(df_coeff,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
trace_actual = go.Scatter(x=x,
y=y,
name='Y Actual',
mode='markers',
marker=dict(color='rgb(106, 181, 135)'))
trace_predict = go.Scatter(x=x,
y=ycap,
name='Y Predicted (ŷ)',
line=dict(width=2, color='rgb(229, 151, 50)'))
ydiff = [y[i] - ycap[i] for i in range(len(y))]
trace_error = go.Scatter(x=x,
y=ydiff,
line=dict(width=2, color='rgb(236, 10, 15)'))
x_title = "x (" + str(x_col[0]) + ")"
y_title = "y,ŷ(" + str(y_col) + ")"
y_ycap_title = go.Layout(title='Actual vs Predicted Y Plot',
hovermode='closest',
xaxis={'title': x_title},
yaxis={'title': y_title})
error_title = go.Layout(title='Error Plot',
hovermode='closest',
xaxis={'title': x_title},
yaxis={'title': 'Error = y - ŷ'})
fig1 = go.Figure(data=[trace_predict, trace_actual], layout=y_ycap_title)
fig2 = go.Figure(data=[trace_error], layout=error_title)
error_mean = html.H2('Error Mean = ' +
str(round(db.get('hor.error_mean'), 4)))
##Team 5 API Integration
anova = get_anova(y, ycap, len(params))
db.put('hor.anova', anova)
anova_div = common.get_anova_div(anova)
independent_var = ','.join(x_col)
return (common.success_msg(
"Higher Order Regression API Exceuted Successfully!!"), table1, table2,
fig1, fig2, error_mean, anova_div, html.H2(independent_var))
def stats_table_and_linear_regression(json_ordered_data):
if json_ordered_data is None:
return (common.msg(None), generate_table(pd.DataFrame(columns=[])),
generate_table(pd.DataFrame(columns=[])), y_ycap_fig,
error_fig, "", "", "")
dff = pd.read_json(json_ordered_data, orient='split')
col = list(dff.columns)
x_col = col[:-1]
y_col = col[-1]
if len(x_col) == 1:
dff = dff.sort_values(by=x_col)
data = [[] for i in range(len(x_col))]
for i in range(len(x_col)):
x = dff[x_col[i]].values.tolist()
data[i] = x
y = list(dff[col[-1]])
##Team 3 API Integration
try:
model = LinearRegression()
db.put("lr.model", model)
db.put("lr.x_col", x_col)
db.put("lr.y_col", y_col)
(summary, params, ycap) = model.fit(data, y)
db.put("lr.summary", summary)
db.put("lr.params", params)
db.put("lr.ycap", ycap)
error_mean = model.model_stats()['mean']
db.put("lr.error_mean", error_mean)
except (Exception, ValueError) as e:
return (common.error_msg("Linear Regression API Error: " + str(e)),
generate_table(pd.DataFrame(columns=[])),
generate_table(pd.DataFrame(columns=[])), y_ycap_fig,
error_fig, "", "", "")
df_stats = common.get_stats_df(summary, x_col, y_col)
table1 = dbc.Table.from_dataframe(df_stats,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
df_coeff = common.get_coeff_df(params, x_col)
table2 = dbc.Table.from_dataframe(df_coeff,
striped=True,
bordered=True,
hover=True,
style=common.table_style)
if len(data) == 1:
trace_x = data[0]
x_title = "x (" + str(x_col[0]) + ")"
trace_actual = go.Scatter(x=trace_x,
y=y,
name='Y Actual',
mode='markers',
marker=dict(color='rgb(106, 181, 135)'))
else:
trace_x = list(range(len(y)))
x_title = 'Sequence of data points'
trace_actual = go.Scatter(x=trace_x,
y=y,
name='Y Actual',
line=dict(width=2,
color='rgb(106, 181, 135)'))
trace_predict = go.Scatter(x=trace_x,
y=ycap,
name='Y Predicted (ŷ)',
line=dict(width=2, color='rgb(229, 151, 50)'))
ydiff = [y[i] - ycap[i] for i in range(len(y))]
trace_error = go.Scatter(x=trace_x,
y=ydiff,
line=dict(width=2, color='rgb(236, 10, 15)'))
y_title = "y,ŷ(" + str(y_col) + ")"
y_ycap_title = go.Layout(title='Actual vs Predicted Y Plot',
hovermode='closest',
xaxis={'title': x_title},
yaxis={'title': y_title})
error_title = go.Layout(title='Error Plot',
hovermode='closest',
xaxis={'title': x_title},
yaxis={'title': 'Error = y - ŷ'})
fig1 = go.Figure(data=[trace_predict, trace_actual], layout=y_ycap_title)
fig2 = go.Figure(data=[trace_error], layout=error_title)
error_mean = html.H2('Error Mean = ' +
str(round(db.get('lr.error_mean'), 4)))
##Team 5 API Integration
anova = get_anova(y, ycap, len(params))
db.put('lr.anova', anova)
anova_div = common.get_anova_div(anova)
independent_var = ','.join(x_col)
return (
common.success_msg("Linear Regression API Exceuted Successfully!!"),
table1, table2, fig1, fig2, error_mean, anova_div,
html.H2(independent_var))