import plotly.graph_objs as go
import traceback
import pandas as pd
import numpy as np
from ml.ux.app import app
from ml.ux.apps import common
from ml.framework.database import db
from ml.framework.file_utils import FileUtils
from ml.framework.data_utils import DataUtils
from ml.non_linear_classification import non_separable_train
layout = html.Div([
common.navbar("Classification - Linearly Non-Separable"),
html.Div([], style = {'padding': '30px'}),
html.Br(),
html.Div([
html.H2("Load and Select a file from all the cleaned files:"),
dbc.Button("Load Cleaned File", color="primary", id = 'nlcl-load-cleaned-files', className="mr-2", style={'display': 'inline-block'}),
dbc.Button("Clear", color="secondary", id = 'nlcl-clear-db', className="mr-2", style={'display': 'inline-block'})
],style = {'margin': '10px'}),
html.Div([
dcc.Dropdown(
id = 'nlcl-selected-cleaned-file',
options = common.get_options('clean'),
value = None,
multi = False
)],
style = {'margin': '10px', 'width': '50%'}),
import pandas as pd
import numpy as np
from ml.ux.app import app
from ml.ux.apps import common
from ml.framework.database import db
from ml.framework.file_utils import FileUtils
from ml.framework.data_utils import DataUtils
from ml.knn import knn_predict
from ml.knn import load_input_csv
from ml.knn import calculate_predict_accuracy
layout = html.Div([
common.navbar("K Nearest Neighbors (KNN)"),
html.Div([], style = {'padding': '30px'}),
html.Br(),
html.Div([
html.H2("Load and Select a file from all the cleaned files:"),
dbc.Button("Load Cleaned File", color="primary", id = 'knn-load-cleaned-files', className="mr-2", style={'display': 'inline-block'}),
dbc.Button("Clear", color="secondary", id = 'knn-clear-db', className="mr-2", style={'display': 'inline-block'})
],style = {'margin': '10px'}),
html.Div([
dcc.Dropdown(
id = 'knn-selected-cleaned-file',
options = common.get_options('clean'),
value = None,
multi = False
)],
style = {'margin': '10px', 'width': '50%'}),
from ml.ux.app import app
from ml.ux.apps import common
from ml.framework.database import db
from ml.framework.file_utils import FileUtils
from ml.framework.data_utils import DataUtils
from ml.stochastic_neural_net import ann_training
from ml.stochastic_neural_net import ann_testing
from ml.stochastic_neural_net import ann_predict
from ml.stochastic_neural_net_2_layer import ann_training_h2
from ml.stochastic_neural_net_2_layer import ann_testing_h2
from ml.stochastic_neural_net_2_layer import ann_predict_h2
layout = html.Div([
common.navbar("Stochastic Gradient Descent"),
html.Div([], style={'padding': '30px'}),
html.Br(),
html.Div([
html.H2("Load and Select a file from all the cleaned files:"),
dbc.Button("Load Cleaned File",
color="primary",
id='sgd-load-cleaned-files',
className="mr-2",
style={'display': 'inline-block'}),
dbc.Button("Clear",
color="secondary",
id='sgd-clear-db',
className="mr-2",
style={'display': 'inline-block'})
],
import dash_core_components as dcc
import dash_bootstrap_components as dbc
import dash_html_components as html
from dash.dependencies import Input, Output
from ml.ux.app import app
from ml.ux.apps import common
from ml.framework.file_utils import FileUtils
from ml.framework.data_utils import DataUtils
from ml.framework.database import db
layout = html.Div([
common.navbar("Home"),
html.Br(),
html.H3(
children=
'A tool developed as part of IISc CCE Machine Learning Course, 2020',
style={'textAlign': 'center'}),
html.Hr(),
html.Br(),
dcc.Upload(id='upload-data',
children=html.Div([html.A('Drag and Drop or Select Files')],
style={'font-size': '16px'}),
style={
'width': '50%',
'height': '50px',
'lineHeight': '50px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
import plotly.graph_objs as go
import traceback
import pandas as pd
import numpy as np
from ml.ux.app import app
from ml.ux.apps import common
from ml.framework.database import db
from ml.framework.file_utils import FileUtils
from ml.framework.data_utils import DataUtils
from ml.decision_tree.main import train
layout = html.Div([
common.navbar("Decision Trees"),
html.Div([], style = {'padding': '30px'}),
html.Br(),
html.H2('Decision Tree API Integration for Data Set banknote.csv'),
html.Div([],id = "decision-trees-new-selected-div")
])
@app.callback(
Output("decision-trees-new-selected-div", "children"),
[Input('decision-trees', 'value')]
)
def dtn_display_selected_file_scatter_plot(value):
value = "banknote"
db.put("dtn.file", value)
file = value
path = FileUtils.path('clean', file)
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from ml.ux.app import app
from ml.ux.apps import common
from ml.framework.database import db
from ml.framework.file_utils import FileUtils
from ml.framework.data_utils import DataUtils
from ml.pca import perform_pca
from ml.pca import dot_product
layout = html.Div([
common.navbar("Principle Component Analysis (PCA)"),
html.Div([], style={'padding': '30px'}),
html.Br(),
html.Div([
html.H2("Load and Select a file from all the cleaned files:"),
dbc.Button("Load Cleaned File",
color="primary",
id='pca-load-cleaned-files',
className="mr-2",
style={'display': 'inline-block'}),
dbc.Button("Clear",
color="secondary",
id='pca-clear-db',
className="mr-2",
style={'display': 'inline-block'})
],