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
if format is None:
div = None
elif (format == 'csv' or format == 'txt') and header is None:
div = common.error_msg('Please Select Header!!')
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)
db.put("data", df)
msg = "Following Properties Applied. Separator=" + sep + " Header=" + str(
header)
table = dbc.Table.from_dataframe(df.head(10),
striped=True,
bordered=True,
hover=True,
style=common.table_style)
div = [common.msg(msg), table]
return div
def display_data(value):
"""Displaying the head for the selected file."""
db_value = db.get("file")
if value is None and db_value is None:
return ""
elif value is None and not db_value is None:
value = db_value
elif not value == db_value:
db.reset()
format = FileUtils.file_format(value)
if format == 'csv' or format == 'txt':
path = FileUtils.path('raw', value)
head = DataUtils.read_text_head(path)
table_col = [html.Col(style = {'width':"10%"}), html.Col(style = {'width':"90%"})]
table_header = [html.Thead(html.Tr([html.Th("Row No"), html.Th("Data")]))]
rows = []
for i in range(len(head)):
row = html.Tr([html.Td(i+1), html.Td(head[i])])
rows.append(row)
table_body = [html.Tbody(rows)]
table = dbc.Table(table_col+ table_header + table_body, bordered=True, style = common.table_style)
div = [common.msg("Selected File: " + value),
common.msg("Selected Format: " + format),
table,
html.Br(),
csv_properties_div]
elif format == 'jpeg' or format == 'jpg' or format == 'gif':
div = [common.msg("Selected File: " + value),
common.msg("Selected Format: " + format)]
else:
div = "Format Not Supported!!"
db.put("file", value)
db.put("format", format)
return div
def sgd_model_predict(n_clicks):
var = db.get('sgd.model_variables')
predict_data = db.get("sgd.model_prediction_data")
summary = db.get('sgd.model_summary')
model = db.get('sgd.model')
yu = db.get('sgd.model_yu')
n_var = len(var)
if predict_data is None:
return ("", "")
if len(predict_data.split(',')) != n_var:
return (common.error_msg('Enter Valid Prediction Data!!'), "")
try:
layer = db.get("sgd.model_layer")
if layer == 1:
feature_vector = get_predict_data_list(predict_data)
df = pd.DataFrame(columns=var)
df.loc[0] = feature_vector
prediction = ann_predict(df, model, yu)
elif layer == 2:
feature_vector = get_predict_data_list(predict_data)
prediction = ann_predict_h2(feature_vector, model, yu)
reverse_quantized_classes = db.get('sgd.reverse_quantized_classes')
prediction = reverse_quantized_classes[int(prediction)]
db.put('sgd.prediction', prediction)
except Exception as e:
traceback.print_exc()
return (common.error_msg("Exception during prediction: " + str(e)), "")
return common.success_msg('Predicted/Classified Class = ' + prediction)
def knn_model_predict(n_clicks):
c = db.get('knn.model_class')
predict_data = db.get("knn.model_prediction_data")
var = db.get('knn.model_variables')
n_var = len(var)
k = db.get('knn.distance')
train_df = db.get('knn.data_train')
if predict_data is None:
return ("" , "")
if len(predict_data.split(',')) != n_var:
return (common.error_msg('Enter Valid Prediction Data!!'), "")
try:
cols = [] + var
cols.append(c)
train_dataset = train_df[cols].astype(str).values.tolist()
feature_vector = get_predict_data_list(predict_data)
feature_vector.append('')
feature_vector = [feature_vector]
result = knn_predict(train_dataset, feature_vector, k)
prediction = result[0][-1]
print(prediction)
db.put('knn.prediction', prediction)
except Exception as e:
traceback.print_exc()
return (common.error_msg("Exception during prediction: " + str(e)), "")
df = db.get('knn.data_train')
df = df.iloc[:, :-1]
div = html.Div([
html.Div([html.H2("Predicted & Training Data Set Scatter Plot")], style={'width': '100%', 'display': 'flex', 'align-items': 'center', 'justify-content': 'center'}),
dbc.Row([
dbc.Col([
dbc.Label("Select X Axis"),
dcc.Dropdown(
id = 'knn-x-axis-predict',
options=[{'label':col, 'value':col} for col in [*df]],
value=None,
multi=False
),
html.Br(),
dbc.Label("Select Y Axis"),
dcc.Dropdown(
id = 'knn-y-axis-predict',
options=[{'label':col, 'value':col} for col in [*df]],
value=None,
multi=False
),
html.Br(),
dbc.Button("Plot", color="primary", id = 'knn-predict-scatter-plot-button'),
html.Div([], id = "knn-x-axis-predict-do-nothing"),
html.Div([], id = "knn-y-axis-predict-do-nothing")
], md=2,
style = {'margin': '10px', 'font-size': '16px'}),
dbc.Col([], md=9, id="knn-scatter-plot-predict")
]),
])
return (common.success_msg('Predicted/Classified Class = ' + prediction), div)
def dtn_model_train(n_clicks):
c = db.get('dtn.model_class')
var = db.get('dtn.model_variables')
max_depth = db.get('dtn.max_depth')
min_size = db.get('dtn.min_size')
folds = 5
if c is None or var is None or max_depth is None or min_size is None:
div = ""
elif (not c is None) and (not var is None) and (not max_depth is None) and (not min_size is None):
try:
path = FileUtils.path('extra', 'banknote.csv')
tree, avg_score, avg_f1_score = train(path, max_depth, min_size, folds)
summary = {}
summary['Max Depth'] = max_depth
summary['Min Size'] = min_size
summary['Folds'] = folds
summary['Average Score'] = round(avg_score, 4)
summary['Average F1 Score'] = round(avg_f1_score, 4)
summary_df = pd.DataFrame(summary.items(), columns=['Parameters', 'Value'])
db.put('dtn.model_summary', summary)
db.put('dtn.model_instance', tree)
except Exception as e:
traceback.print_exc()
return common.error_msg("Exception during training model: " + str(e))
div = html.Div([
html.H2('Model Parameters & Summary:'),
dbc.Table.from_dataframe(summary_df, striped=True, bordered=True, hover=True, style = common.table_style),
html.Br(),
html.H2('Tree'),
html.H2(str(tree)),
])
else:
div = common.error_msg('Select Proper Model Parameters!!')
return div
def dt_model_predict(n_clicks):
var = db.get('dt.model_variables')
predict_data = db.get("dt.model_prediction_data")
model = db.get('dt.model_instance')
n_var = len(var)
if predict_data is None:
return ("" , "")
if len(predict_data.split(',')) != n_var:
return (common.error_msg('Enter Valid Prediction Data!!'), "")
try:
feature_vector = get_predict_data_list(predict_data)
feature_vector.append(-1)
feature_vector = [feature_vector]
prediction = model.predict(feature_vector)
print(prediction)
prediction = str(prediction[0])
db.put('dt.prediction', prediction)
except Exception as e:
traceback.print_exc()
return (common.error_msg("Exception during prediction: " + str(e)), "")
return common.success_msg('Predicted/Classified Class = ' + prediction)
def dt_display_selected_file_scatter_plot(value):
db_value = db.get("dt.file")
if value is None and db_value is None:
return common.msg("Please select a cleaned file to proceed!!")
elif value is None and not db_value is None:
value = db_value
db.put("dt.file", value)
file = value
path = FileUtils.path('clean', file)
df = DataUtils.read_csv(path)
db.put("dt.data", df)
div = html.Div([
common.msg("Selected cleaned file: "+ file),
dbc.Table.from_dataframe(df.head(10).astype(str), striped=True, bordered=True, hover=True, style = common.table_style),
#html.Div([html.H3("Data Statistics")], style={'width': '100%', 'display': 'flex', 'align-items': 'center', 'justify-content': 'center'}),
#dbc.Table.from_dataframe(stats, striped=True, bordered=True, hover=True, style = common.table_style),
html.Br(),
get_dt_model_properties_div(df),
html.Div([], id = "dt-trained-model", style = {'margin': '10px'}),
])
return div
def dtn_display_selected_file_scatter_plot(value):
value = "banknote"
db.put("dtn.file", value)
file = value
path = FileUtils.path('clean', file)
df = DataUtils.read_csv(path)
save_path = FileUtils.path('extra', 'banknote.csv')
df.to_csv(save_path, index=False, header = False)
db.put("dtn.data", df)
db.put('dtn.model_class', 'class')
db.put('dtn.model_variables', ['variance','skewness','curtosis','entropy'])
call_path = FileUtils.path('nets', 'dt_banknote_call1.csv')
cdf = DataUtils.read_csv(call_path)
trace_1 = go.Scatter(x = cdf['max_depth'], y = cdf['avg_train_score'], name = 'Average Train Score')
trace_2 = go.Scatter(x = cdf['max_depth'], y = cdf['avg_test_score'], name = 'Average Test Score')
title = go.Layout(title = 'Depth of Tree Vs Performance Plot', hovermode = 'closest', xaxis={'title': 'Depth of Tree'}, yaxis={'title': 'Performance'})
fig = go.Figure(data = [trace_1, trace_2], layout = title)
div = html.Div([
common.msg("Selected cleaned file: "+ file),
dbc.Table.from_dataframe(df.head(10).round(5).astype(str), striped=True, bordered=True, hover=True, style = common.table_style),
html.Br(),
html.H2('Using Default parameters for both max_depth and min_size.'),
html.H2('Max Depth = 2 to 15'),
html.H2('Min Size = 10'),
dbc.Table.from_dataframe(cdf.round(4), striped=True, bordered=True, hover=True, style = common.table_style),
html.Br(),
dcc.Graph(id='dtn-plot', figure=fig),
html.Br(),
get_dtn_model_properties_div(df),
dcc.Loading(id="dtn-model-training",
children=[html.Div([], id = "dtn-trained-model", style = {'margin': '10px'})],
type="default"),
])
return div
def knn_model_prediction_data(value):
if not value is None:
db.put("knn.model_prediction_data", value)
return None
def knn_model_train(n_clicks):
c = db.get('knn.model_class')
var = db.get('knn.model_variables')
train = db.get('knn.model_train')
k = db.get('knn.distance')
file = db.get("knn.file")
if c is None and var is None and train is None and k is None:
div = ""
elif train is None or train < 0 or train > 100:
div = common.error_msg('Training % should be between 0 - 100 !!')
elif (not c is None) and (not var is None) and (not train is None) and (not k is None):
try:
cols = [] + var
cols.append(c)
df = db.get('knn.data')
df = df[cols]
train_df, test_df = common.split_df(df, c, train)
distinct_count_df_total = get_distinct_count_df(df, c, 'Total Count')
distinct_count_df_train = get_distinct_count_df(train_df, c, 'Training Count')
distinct_count_df_test = get_distinct_count_df(test_df, c, 'Testing Count')
distinct_count_df = distinct_count_df_total.join(distinct_count_df_train.set_index('Class'), on='Class')
distinct_count_df = distinct_count_df.join(distinct_count_df_test.set_index('Class'), on='Class')
train_dataset = train_df[cols].astype(str).values.tolist()
test_dataset = test_df[cols].astype(str).values.tolist()
result = knn_predict(train_dataset, test_dataset, k)
cc_percentage = calculate_predict_accuracy(result)
summary = {}
summary['Total Training Data'] = len(train_df)
summary['Total Testing Data'] = len(test_df)
summary['Total Number of Features in Dataset'] = len(var)
summary['Model Accuracy %'] = round(cc_percentage, 2)
summary['Features'] = str(var)
summary_df = pd.DataFrame(summary.items(), columns=['Parameters', 'Value'])
db.put('knn.data_train', train_df)
db.put('knn.data_test', test_df)
db.put('knn.model_summary', summary)
classes = df[c].unique()
confusion_df = get_confusion_matrix(result, classes)
except Exception as e:
traceback.print_exc()
return common.error_msg("Exception during training model: " + str(e))
div = html.Div([
html.H2('Class Grouping in Data:'),
dbc.Table.from_dataframe(distinct_count_df, striped=True, bordered=True, hover=True, style = common.table_style),
html.H2('Model Parameters & Summary:'),
dbc.Table.from_dataframe(summary_df, striped=True, bordered=True, hover=True, style = common.table_style),
html.H2('Confusion Matrix (Precision & Recall):'),
dbc.Table.from_dataframe(confusion_df, striped=True, bordered=True, hover=True, style = common.table_style),
html.H2('Prediction/Classification:'),
html.P('Features to be Predicted (comma separated): ' + ','.join(var), style = {'font-size': '16px'}),
dbc.Input(id="knn-prediction-data", placeholder=','.join(var), type="text"),
html.Br(),
dbc.Button("Predict", color="primary", id = 'knn-predict'),
html.Div([], id = "knn-prediction"),
html.Div([],id = "knn-predicted-scatter-plot")
])
else:
div = common.error_msg('Select Proper Model Parameters!!')
return div
def knn_model_lr(value):
if not value is None:
db.put("knn.distance", value)
return None
def knn_model_train(value):
if not value is None:
db.put("knn.model_train", value)
return None
def sgd_model_train(value):
if not value is None:
db.put("sgd.model_train", value)
return None
def nlcl_model_class(value):
if not value is None:
db.put("nlcl.model_class", value)
return None
def nlcl_model_prediction_data(value):
if not value is None:
db.put("nlcl.model_prediction_data", value)
return None
def nlcl_model_train(value):
if not value is None:
db.put("nlcl.model_train", value)
return None
def sgd_model_prediction_data(value):
if not value is None:
db.put("sgd.model_prediction_data", value)
return None
def sgd_model_train(n_clicks):
c = db.get('sgd.model_class')
var = db.get('sgd.model_variables')
train = db.get('sgd.model_train')
#test = db.get('sgd.model_test')
lr = db.get('sgd.model_lr')
epoch = db.get('sgd.model_epoch')
#no_of_hidden_layer = db.get('sgd.no_of_hidden_layer')
no_of_neuron = db.get('sgd.no_of_neuron')
no_of_neuron_h2 = db.get('sgd.no_of_neuron_h2')
layer = 1
if not no_of_neuron_h2 is None:
layer = 2
db.put("sgd.model_layer", layer)
if c is None and var is None and train is None and lr is None and epoch is None:
div = ""
elif train is None or train < 0 or train > 100:
div = common.error_msg('Training % should be between 0 - 100 !!')
elif (not c is None) and (not var is None) and (not train is None) and (
not lr is None) and (not epoch is None):
try:
cols = [] + var
cols.append(c)
df = db.get('sgd.data')
df = df[cols]
## Make DataFrame compatible for SGD API ##
df, quantized_classes, reverse_quantized_classes = quantized_class(
df, c)
train_df, test_df = common.split_df(df, c, train)
distinct_count_df_total = get_distinct_count_df(
df, c, 'Total Count')
distinct_count_df_train = get_distinct_count_df(
train_df, c, 'Training Count')
distinct_count_df_test = get_distinct_count_df(
test_df, c, 'Testing Count')
distinct_count_df = distinct_count_df_total.join(
distinct_count_df_train.set_index('Class'), on='Class')
distinct_count_df = distinct_count_df.join(
distinct_count_df_test.set_index('Class'), on='Class')
distinct_count_df['Class'] = distinct_count_df['Class'].map(
reverse_quantized_classes)
if layer == 1:
ycap, loss_dict, cc_percentage, wc_percentage, model, yu = ann_training(
train_df[var], train_df[c], no_of_neuron, lr, epoch)
ycap, cc_percentage, wc_percentage = ann_testing(
test_df[var], test_df[c], model, yu)
elif layer == 2:
ycap, loss_dict, cc_percentage, wc_percentage, model, yu = ann_training_h2(
train_df[var], train_df[c], no_of_neuron, no_of_neuron_h2,
lr, epoch)
ycap, cc_percentage, wc_percentage = ann_testing_h2(
train_df[var], train_df[c], model, yu)
summary = {}
summary['Total Training Data'] = len(train_df)
summary['Total Testing Data'] = len(test_df)
summary['Total Number of Features in Dataset'] = len(var)
summary['Total no of Layers'] = layer + 2
summary['No of Hidden Layer'] = layer
summary['No of Neuron in Hidden Layer 1'] = no_of_neuron
summary['No of Neuron in Hidden Layer 2'] = no_of_neuron_h2
summary['Activation Function'] = 'Sigmoid'
summary['Learning rate'] = lr
summary['Epochs'] = epoch
summary['Model Accuracy'] = round(cc_percentage, 2)
summary['Features'] = str(var)
summary_df = pd.DataFrame(summary.items(),
columns=['Parameters', 'Value'])
db.put('sgd.data_train', train_df)
db.put('sgd.data_test', test_df)
db.put('sgd.quantized_classes', quantized_classes)
db.put('sgd.reverse_quantized_classes', reverse_quantized_classes)
db.put('sgd.model', model)
db.put('sgd.model_yu', yu)
db.put('sgd.summary', summary)
confusion_df = get_confusion_matrix(test_df, c, var, model, yu,
reverse_quantized_classes)
except Exception as e:
traceback.print_exc()
return common.error_msg("Exception during training model: " +
str(e))
trace = go.Scatter(x=loss_dict['Epoch_no'],
y=loss_dict['Loss'],
line=dict(width=2, color='rgb(106, 181, 135)'))
convergence_title = go.Layout(title='Convergence Plot',
hovermode='closest',
xaxis={'title': 'Epoch'},
yaxis={'title': 'Loss Function'})
convergence_fig = go.Figure(data=[trace], layout=convergence_title)
div = html.Div([
html.H2('Class Grouping in Data:'),
dbc.Table.from_dataframe(distinct_count_df,
striped=True,
bordered=True,
hover=True,
style=common.table_style),
html.H2('Model Parameters & Summary:'),
dbc.Table.from_dataframe(summary_df,
striped=True,
bordered=True,
hover=True,
style=common.table_style),
html.Br(),
dcc.Graph(id='sgd-convergence-plot', figure=convergence_fig),
html.H2('Confusion Matrix (Precision & Recall):'),
dbc.Table.from_dataframe(confusion_df,
striped=True,
bordered=True,
hover=True,
style=common.table_style),
html.Br(),
html.Br()
])
else:
div = common.error_msg('Select Proper Model Parameters!!')
return div
def sgd_model_epoch(value):
if not value is None:
db.put("sgd.model_epoch", value)
return None
def sgd_model_lr(value):
if not value is None:
db.put("sgd.model_lr", value)
return None
def sgd_model_neuron_h2(value):
if not value is None:
db.put("sgd.no_of_neuron_h2", value)
return None
def knn_y_axis(value):
if not value is None:
db.put("knn.y_axis_predict", value)
return None
def nlcl_y_axis(value):
if not value is None:
db.put("nlcl.y_axis", value)
return None
def knn_model_variables(value):
if not value is None:
db.put("knn.model_variables", value)
return None
def nlcl_model_variables(value):
if not value is None:
db.put("nlcl.model_variables", value)
return None
def sgd_model_variables(value):
if not value is None:
db.put("sgd.model_variables", value)
return None
def nlcl_model_train(n_clicks):
c = db.get('nlcl.model_class')
var = db.get('nlcl.model_variables')
train = db.get('nlcl.model_train')
if c is None and var is None and train is None:
div = ""
elif train is None or train < 0 or train > 100:
div = common.error_msg('Training % should be between 0 - 100 !!')
elif len(var) != 2:
div = common.error_msg('Select Two Features!!')
elif (not c is None) and (not var is None) and (not train is None):
try:
cols = [] + var
cols.append(c)
df = db.get('nlcl.data')
df = df[cols]
train_df, test_df = common.split_df(df, c, train)
train_df.columns = ['X1', 'X2', 'Class']
distinct_count_df_total = get_distinct_count_df(df, c, 'Total Count')
distinct_count_df_train = get_distinct_count_df(train_df, c, 'Training Count')
distinct_count_df_test = get_distinct_count_df(test_df, c, 'Testing Count')
distinct_count_df = distinct_count_df_total.join(distinct_count_df_train.set_index('Class'), on='Class')
distinct_count_df = distinct_count_df.join(distinct_count_df_test.set_index('Class'), on='Class')
model = non_separable_train(train_df)
print(model)
summary = {}
summary['Total Training Data'] = len(train_df)
summary['Total Testing Data'] = len(test_df)
summary['Total Number of Features in Dataset'] = len(var)
summary['Model Accuracy %'] = 'TODO'
summary['Features'] = str(var)
summary_df = pd.DataFrame(summary.items(), columns=['Parameters', 'Value'])
db.put('nlcl.data_train', train_df)
db.put('nlcl.data_test', test_df)
db.put('nlcl.model_summary', summary)
db.put('nlcl.model_instance', model)
#confusion_df = get_confusion_matrix(test_df, c, var, instanceOfLR)
except Exception as e:
traceback.print_exc()
return common.error_msg("Exception during training model: " + str(e))
clazz_col = c
train_df.columns = cols
df = train_df
x_col = var[0]
y_col = var[1]
x1, y1 = get_rect_coordinates(model[0])
x2, y2 = get_rect_coordinates(model[1])
x3, y3 = get_rect_coordinates(model[2])
graph_data = [
go.Scatter(
x=df[df[clazz_col] == clazz][x_col],
y=df[df[clazz_col] == clazz][y_col],
text=df[df[clazz_col] == clazz][clazz_col],
mode='markers',
opacity=0.8,
marker={
'size': 15,
'line': {'width': 0.5, 'color': 'white'}
},
name=clazz
) for clazz in df[clazz_col].unique()
]
graph_data.append(go.Scatter(x=x1, y=y1, text = 'Specific Rectangle', name = 'Specific Rectangle'))
graph_data.append(go.Scatter(x=x3, y=y3, text = 'Optimal Rectangle', name = 'Optimal Rectangle'))
graph_data.append(go.Scatter(x=x2, y=y2, text = 'Generic Rectangle', name = 'Generic Rectangle'))
graph = dcc.Graph(
id='nlcl-x-vs-y-rectangle',
figure={
'data': graph_data,
'layout': dict(
title='Boundaries & Train Data Set Scatter Plot',
xaxis={'title': x_col},
yaxis={'title': y_col},
margin={'l': 40, 'b': 40},
legend={'x': 0, 'y': 1},
hovermode='closest'
)
}
)
div = html.Div([
html.H2('Class Grouping in Data:'),
dbc.Table.from_dataframe(distinct_count_df, striped=True, bordered=True, hover=True, style = common.table_style),
html.H2('Model Parameters & Summary:'),
dbc.Table.from_dataframe(summary_df, striped=True, bordered=True, hover=True, style = common.table_style),
html.Br(),
graph,
#html.H2('Confusion Matrix (Precision & Recall):'),
#dbc.Table.from_dataframe(confusion_df, striped=True, bordered=True, hover=True, style = common.table_style),
html.H2('Prediction/Classification:'),
html.P('Features to be Predicted (comma separated): ' + ','.join(var), style = {'font-size': '16px'}),
dbc.Input(id="nlcl-prediction-data", placeholder=','.join(var), type="text"),
html.Br(),
dbc.Button("Predict", color="primary", id = 'nlcl-predict'),
html.Div([], id = "nlcl-prediction"),
html.Div([],id = "nlcl-predicted-scatter-plot")
])
else:
div = common.error_msg('Select Proper Model Parameters!!')
return div
def knn_x_axis(value):
if not value is None:
db.put("knn.x_axis", value)
return None
def nlcl_display_selected_file_scatter_plot(value):
db_value = db.get("nlcl.file")
if value is None and db_value is None:
return common.msg("Please select a cleaned file to proceed!!")
elif value is None and not db_value is None:
value = db_value
db.put("nlcl.file", value)
file = value
path = FileUtils.path('clean', file)
df = DataUtils.read_csv(path)
db.put("nlcl.data", df)
stats = df.describe(include = 'all').head(6).round(5)
stats.insert(loc=0, column='Statistics', value=['Count','unique','top','freq','Mean','Standard Deviation'])
stats = stats.drop(stats.index[[1,2,3]])
div = html.Div([
common.msg("Selected cleaned file: "+ file),
dbc.Table.from_dataframe(df.head(10), striped=True, bordered=True, hover=True, style = common.table_style),
html.Div([html.H3("Data Statistics")], style={'width': '100%', 'display': 'flex', 'align-items': 'center', 'justify-content': 'center'}),
dbc.Table.from_dataframe(stats, striped=True, bordered=True, hover=True, style = common.table_style),
html.Br(),
html.Div([html.H2("Scatter Plot")], style={'width': '100%', 'display': 'flex', 'align-items': 'center', 'justify-content': 'center'}),
dbc.Row([
dbc.Col([
dbc.Label("Select Class"),
dcc.Dropdown(
id = 'nlcl-class',
options=[{'label':col, 'value':col} for col in [*df]],
value=None,
multi=False
),
html.Br(),
dbc.Label("Select X Axis"),
dcc.Dropdown(
id = 'nlcl-x-axis',
options=[{'label':col, 'value':col} for col in [*df]],
value=None,
multi=False
),
html.Br(),
dbc.Label("Select Y Axis"),
dcc.Dropdown(
id = 'nlcl-y-axis',
options=[{'label':col, 'value':col} for col in [*df]],
value=None,
multi=False
),
html.Br(),
dbc.Button("Plot", color="primary", id = 'nlcl-scatter-plot-button'),
html.Div([], id = "nlcl-class-do-nothing"),
html.Div([], id = "nlcl-x-axis-do-nothing"),
html.Div([], id = "nlcl-y-axis-do-nothing")
], md=2,
style = {'margin': '10px', 'font-size': '16px'}),
dbc.Col([], md=9, id="nlcl-scatter-plot")
]),
html.Br(),
get_nlcl_model_properties_div(df),
html.Div([], id = "nlcl-trained-model", style = {'margin': '10px'}),
])
return div
def knn_model_class(value):
if not value is None:
db.put("knn.model_class", value)
return None
