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control_cards.py
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import dash_bootstrap_components as dbc
from dash import dcc, html, dash_table
import binary_classify
import DecisionTree
import GLM
import IMMR
import MLR
import quantile_regression
import Single_InDepth
import survival
import choose_data
statsmodels_tags = ["AIDS ","Down","WHO ","abortion","adolescent","adult","anxiety","arthritis",
"auditory","autism","behavior","biopsy","birth","blood","body","breast",
"cancer","cardiovascular","cell","child","cholera","cirrhosis","clinical",
"coma","consumption","contagious","contraceptive","coronary","covid","deaths",
"dengue","dependency","deprivation","diabetes","diarrhaea","disease","doctor",
"donation","drug","eating","ebola","efficacy","exercise","feet","fertility",
"food","freedom","gestation","headaches","health","healthcare","hepatocellular",
"hiv","illness","indomethacin","infant","insurance","liver","male","malignant",
"mammogram","marrow","medicaid","medical","medicare","medpar","melanoma",
"migraine","neuro","nutrition","obesity","obstetrics","organ","pancreatic",
"parents","periodontal","physician","prenatal","prostate","prothrombin",
"quarantine","radiation","recovery","remission","reporting","respiratory",
"risk","serum","sleep","smallpox","smoking","society","stress","syndrome",
"transplant","urine","vaccine","weight","wheeze","women"]
####################################################################################################
#################### DASH APP CONTROL CARDS #############################################
####################################################################################################
def description_card1():
return html.Div(
children=[
html.Div(
children=[
html.H3("Regression workbench",
style={'textAlign': 'left',
'margin-left': '2%',
'color': '#2a8cff',
}
),
dcc.Markdown("Discover relationships within data using the most common tool " +
"of statistical analysis. This open-source analytical " +
"application offers simple and sophisticated forms of " +
"regression, automated analyses and optimizations, and provides " +
"user guidance and interpretive outputs. Use the web application" +
" or download the " +
"[source code] (https://github.com/klocey/regression-workbench)" +
" and run it locally.",
style={'textAlign': 'left',
'margin-left': '3%',
},
),
html.Br(),
control_card_upload1(),
inspect_data_table(),
control_card_choose_reg1(),
],
style={ 'width': '99%',
'display': 'inline-block',
},
),
],
)
def description_card_final():
"""
:return: A Div containing dashboard title & descriptions.
"""
return html.Div(
id="description-card-final",
children=[
html.H5("Developer",
style={
'textAlign': 'left',
},
),
html.P("Kenneth J. Locey, PhD. Senior clinical data scientist. Center for Quality, " +
"Safety and Value Analytics. Rush University Medical Center.",
style={
'textAlign': 'left',
},
),
html.H5("Testers",
style={
'textAlign': 'left',
},
),
html.P("Ryan Schipfer. Senior clinical data scientist. Center for Quality, Safety " +
"and Value Analytics. Rush University Medical Center.",
style={
'textAlign': 'left',
},
),
html.P("Brittnie Dotson. Clinical data scientist. Center for Quality, Safety and " +
"Value Analytics. Rush University Medical Center.",
style={
'textAlign': 'left',
},
),
],
)
def control_card_upload1():
return html.Div(
id="control-card-upload1",
children=[
dbc.Button("1. load a dataset",
id="open-centered-controlcard_load",
style={
"background-color": "#2a8cff",
'width': '99%',
'font-size': 16,
'display': 'inline-block',
},
),
dbc.Modal(
[dbc.ModalBody([
html.Div(
id="left-column1a",
className="one columns",
children=[control_card_upload1a()],
style={'width': '46.0%',
'display': 'block',
'border-radius': '15px',
'box-shadow': '1px 1px 1px grey',
'background-color': '#f0f0f0',
'padding': '10px',
'margin-bottom': '10px',
},
),
html.Div(
id="left-column1b",
className="one columns",
children=[control_card_upload1b()],
style={'width': '46.0%',
'display': 'block',
'border-radius': '15px',
'box-shadow': '1px 1px 1px grey',
'background-color': '#f0f0f0',
'padding': '10px',
'margin-bottom': '10px',
},
),
html.Br(),
],
),
dbc.ModalFooter(
dbc.Button("Close",
id="close-centered-controlcard_load",
className="ml-auto")
),
],
id="modal-centered-controlcard_load",
is_open=False,
centered=True,
autoFocus=True,
size="xl",
keyboard=True,
fade=True,
backdrop=True,
),
],
style={
'width': '28%',
'margin-left': '5%',
'margin-bottom': '1%',
'display': 'inline-block',
},
)
def control_card_upload1a():
return html.Div(
id="control-card-upload1a",
children=[
html.H5("Option 1. Upload your own data",
style={'display': 'inline-block',
'margin-right': '1%',
},
),
html.I(className="fas fa-question-circle fa-lg",
id="target1a",
style={
'display': 'inline-block',
'width': '5%',
'color':'#99ccff',
},
),
dbc.Tooltip("Uploaded should have a simple format: rows, columns, and one row of " +
"column headers. Headers should not have commas or colons. Data should " +
"not have mixed types (10% and 10cm have numeric and non-numeric " +
"characters).",
target="target1a",
style = {
'font-size': 14,
},
),
html.P("This app only accepts .csv files. Data are deleted when the app is " +
"refreshed, closed, or when another file is uploaded. Still, do not " +
"upload sensitive data.",
),
dcc.Upload(
id='upload-data',
children=html.Div([
'Drag and Drop or ',
html.A('Select a CSV File',
style={'color':'#2c8cff',
"text-decoration": "underline",
},
),
],
),
style={
'lineHeight': '34px',
'borderWidth': '2px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
},
multiple=False,
),
],
)
def control_card_upload1b():
return html.Div(
children=[
html.H5("Option 2. Select a healthcare dataset",
style={'display': 'inline-block',
'margin-right': '5%',
},
),
html.P("These preprocessed healthcare datasets are derived from publicly available " +
"data provided in peer-reviewed publications or by the Centers for Medicare " +
"and Medicaid Services."),
dbc.Button("Select a dataset",
id="open-centered-controlcard",
style={
"background-color": "#2a8cff",
'width': '95%',
'font-size': 12,
},
),
dbc.Modal(
[dbc.ModalBody([choose_data.control_card_choose_data(),
html.Br(),
],
),
],
id="modal-centered-controlcard",
is_open=False,
centered=True,
autoFocus=True,
size="xl",
keyboard=True,
fade=True,
backdrop=True,
),
],
)
def level_grouper_control_card():
return html.Div(
children=[
html.H5('Group Levels',
style={'textAlign': 'left',
'font-size': 28,
'color': '#ffffff',
},
),
html.Br(),
html.B("Choose a categorical variable",
style={'display': 'block',
'font-size': 16,
'vertical-align': 'top',
'color': '#ffffff',
},
),
dcc.Dropdown(
id='cat_var_group',
options=[{"label": i, "value": i} for i in []],
multi=False,
value=None,
style={'width': '50%',
},
),
html.Br(),
html.B("Choose one or more levels",
style={'display': 'block',
'font-size': 16,
'vertical-align': 'top',
'color': '#ffffff',
},
),
dcc.Dropdown(
id='level_vars',
options=[{"label": i, "value": i} for i in []],
multi=True,
value=None,
style={'width': '99%',
},
),
html.Br(),
html.B("Create a new level OR group the level(s) you chose into an existing level",
style={'display': 'block',
'font-size': 16,
'vertical-align': 'top',
'color': '#ffffff',
},
),
dcc.Input(
id='new_level_name',
type='text',
placeholder='Type the name of an existing level or create a new level',
style={'width': '51%',
'font-size': 16,
'display': 'block',
},
),
html.Br(),
dbc.Button('Group',
id='level-group-btn1',
n_clicks=0,
style={'width': '20%',
'font-size': 14,
"background-color": "#2a8cff",
'margin-right': '2%',
},
),
html.Br(),
html.H5("", id='group_text',
style={'textAlign': 'left',
'font-size': 16,
'margin-left': '3%',
'color': '#ffffff'}),
dcc.Interval(id='group-interval-component', interval=3000, n_intervals=0)
],
)
def statsmodels_data_table():
return html.Div(
id='statsmodels_data_table_div',
className="ten columns",
children=[
dash_table.DataTable(
id='statsmodels_data_table',
columns=[{
'name': 'Column {}'.format(i),
'id': 'column-{}'.format(i),
'deletable': False,
'renamable': False,
'selectable': False,
} for i in range(1, 4)],
data=None,
is_focused=True,
#virtualization=True,
#editable=True,
page_action='native',
page_size=120,
filter_action='native',
sort_action='native',
#row_deletable=True,
#column_selectable='multi',
#export_format='xlsx',
#export_headers='display',
fixed_rows={'headers': True},
style_header={'padding':'1px',
#'width':'250px',
#'minWidth':'250px',
#'maxWidth':'250px',
'textAlign': 'center',
'overflowX': 'auto',
},
style_data = {'padding':'5px',
#'width':'250px',
#'minWidth':'250px',
#'maxWidth':'250px',
'textAlign': 'center',
'overflowX': 'auto',
},
style_table={#'height': '120px',
'overflowX': 'auto',
},
style_cell_conditional=[
{'if': {'column_id': 'id'}, 'width': '60%'},
{'if': {'column_id': 'No. of rows'}, 'width': '20%'},
{'if': {'column_id': 'No. of columns'}, 'width': '20%'},
],
),
],
style={'display':'block',
'width': '93%',
"background-color": "#696969",
}
)
def data_table():
return html.Div(
id='Data-Table1',
className="ten columns",
children=[html.H5("Data Table",
style={'display': 'inline-block',
'color': '#FFFFFF',
'font-size': 28,
'width': '10.5%',
},
),
html.I(className="fas fa-question-circle fa-lg",
id="target_DataTable",
style={'display': 'inline-block',
'width': '3%',
'color':'#99ccff',
'vertical-align': 'center',
},
),
dbc.Tooltip("Use this table to ensure your data loaded as expected and to delete any " +
"rows or select, delete, and rename any columns. There are no limits on " +
"dataset size when running the application locally. But, when using the " +
"web application, any dataset containing more than 5K rows or 50 columns " +
"will be randomly sampled to meet those constraints.",
target="target_DataTable",
style = {'font-size': 12,
#'display': 'inline-block',
},
),
html.P("", id='rt4',
style={'color': '#FFFFFF',
'font-size': 18,
},
),
dash_table.DataTable(
id='data_table',
columns=[{
'name': 'Column {}'.format(i),
'id': 'column-{}'.format(i),
'deletable': True,
'renamable': True,
'selectable': True,
} for i in range(1, 9)],
data=None,
#virtualization=True,
editable=True,
page_action='native',
page_size=100,
filter_action='native',
sort_action='native',
#row_deletable=True,
column_selectable='multi',
export_format='xlsx',
export_headers='display',
#fixed_rows={'headers': True},
style_header={'padding':'1px',
'width':'250px',
'minWidth':'250px',
'maxWidth':'250px',
'textAlign': 'center',
'overflowX': 'auto',
},
style_data = {'padding':'5px',
'width':'250px',
'minWidth':'250px',
'maxWidth':'250px',
'textAlign': 'center',
'overflowX': 'auto',
},
style_table={'height': '120px',
'overflowX': 'auto',
},
),
],
style={'display':'block',
'width': '95%',
"background-color": "#696969",
}
)
def inspect_data_table():
return html.Div(
children=[
dbc.Button("2. inspect your data",
id="open-inspect_main_datatable",
style={
"background-color": "#2a8cff",
'width': '99%',
'font-size': 16,
'display': 'inline-block',
},
),
dbc.Modal([
dbc.ModalBody([
html.Div(
id="left-column_inspect_table",
className="one columns",
children=[data_table()],
style={'width': '100%'},
),
html.Br(),
],
style={'width': '100%',
"background-color": "#696969",
},
),
dbc.ModalFooter([
dbc.Button('Group Levels',
id="open-level-group",
style={
"background-color": "#2a8cff",
'width': '30%',
'font-size': 16,
'display': 'inline-block',
},
),
dbc.Modal([
dbc.ModalBody([
html.Div(
id="level-group",
className="one columns",
children=[level_grouper_control_card()],
style={'width': '100%'},
),
html.Br(),
],
style={'width': '100%',
"background-color": "#A0A0A0",
},
),
dbc.ModalFooter(
dbc.Button("Click to Close",
id="close-level-group",
className="ml-auto",
style={
"background-color": "#2a8cff",
'width': '30%',
'font-size': 16,
},
),
style={
"background-color": "#A0A0A0",
"display": "flex",
"justify-content": "center",
"align-items": "center",
},
),
],
id="modal-level-group",
is_open=False,
centered=True,
autoFocus=True,
size="xl",
keyboard=True,
fade=True,
backdrop=True,
),
dbc.Button("Click to Close",
id="close-inspect_main_datatable",
className="ml-auto",
style={
"background-color": "#2a8cff",
'width': '30%',
'font-size': 16,
'display': 'inline-block',
},
),
],
style={
"background-color": "#696969",
"display": "flex",
"justify-content": "center",
"align-items": "center",
},
),
],
id="modal-inspect_main_datatable",
is_open=False,
centered=True,
autoFocus=True,
fullscreen=True,
keyboard=True,
fade=True,
),
],
style={'width': '28%',
'display': 'inline-block',
'margin-left': '3%',
'margin-bottom': '1%',
},
)
def control_card_choose_reg1():
return html.Div(
children=[
dbc.Button("3. choose an analysis",
id="open-choose_regression",
style={
"background-color": "#2a8cff",
'width': '99%',
'font-size': 16,
'display': 'inline-block',
},
),
dbc.Modal(
[dbc.ModalBody([
html.Div(
id="left-column_choose_reg2",
className="one columns",
children=[control_card_choose_reg2()],
style={'width': '100%',
'display': 'inline-block',
},
),
html.Br(),
],
),
dbc.ModalFooter(
dbc.Button("Close",
id="close-choose_regression",
className="ml-auto",
style={"background-color": "#2a8cff",
'width': '30%',
'font-size': 14,
},
),
style={ "background-color": "#696969",
"display": "flex",
"justify-content": "center", # Center horizontally
"align-items": "center", # Center vertically)
},
),
],
id="modal-choose_regression",
is_open=False,
centered=True,
autoFocus=True,
size="xl",
keyboard=True,
fade=True,
backdrop=True,
),
],
style={'width': '28%',
'display': 'inline-block',
'margin-left': '3%',
'margin-bottom': '2%',
},
)
def control_card_choose_reg2():
return html.Div(
children=[
html.Div(
children=[
html.H5("Iterative Multi-Model Regressions",
style={'display': 'inline-block',
},
),
dcc.Markdown("Automate the search for **linear and non-linear** 1-to-1 " +
"relationships. Apply optimized data transformations and choose " +
"between the classic ordinary least squares (**OLS**) approach " +
"or run **Robust regression** to reduce the influence of outliers.",
style={'width': '94.1%',
},
),
dbc.Button("Run iterative multi-model analysis",
id='open-iterative_ols',
style={"background-color": "#2a8cff",
'width': '92.5%',
'font-size': 12,
'display': 'inline-block',
},
),
dbc.Modal([
dbc.ModalBody([
html.Div(
id="left-column2",
className="two columns",
children=[IMMR.control_card_iterative_multi_model_regression(),
IMMR.generate_outputs_iterative_multi_model_regression(),
],
style={'width': '95.3%',
'display': 'block',
'border-radius': '15px',
'box-shadow': '1px 1px 1px grey',
'background-color': '#f0f0f0',
'padding': '10px',
'margin-bottom': '10px',
},
),
]),
dbc.ModalFooter(
dbc.Button("Click to Close",
id='close-iterative_ols',
className="ml-auto",
style={
"background-color": "#2a8cff",
'width': '30%',
'font-size': 14,
},
),
style={
"background-color": "#696969",
"display": "flex",
"justify-content": "center",
"align-items": "center",
},
),
],
id="modal-iterative_ols",
is_open=False,
centered=True,
autoFocus=True,
fullscreen=True,
keyboard=True,
fade=True,
),
],
style={'display': 'inline-block',
'width': '45%',
'margin-right': '7%',
'margin-left': '1%',
},
),
html.Div(
children=[
html.H5("In-depth Bivariate Regression",
style={'display': 'inline-block',
},
),
dcc.Markdown("Focus on the relationship between 2 variables using **linear " +
"and polynomial** forms of **OLS regression** and **Robust " +
"regression**. Perform data transformations, build confidence " +
"intervals and prediction intervals, and identify outliers.",
style={'width': '94.1%',
},
),
dbc.Button("Run in-depth bivariate regression",
id='open-single_ols',
style={
"background-color": "#2a8cff",
'width': '92.5%',
'font-size': 12,
'display': 'inline-block',
},
),
dbc.Modal([
dbc.ModalBody([
html.Div(
id="left-column3",
className="two columns",
children=[Single_InDepth.control_card_single_regression(),
Single_InDepth.generate_figure_single_regression(),
],
style={'width': '95.3%',
'border-radius': '15px',
'box-shadow': '1px 1px 1px grey',
'background-color': '#f0f0f0',
'padding': '10px',
'margin-bottom': '10px',
},
),
]),
dbc.ModalFooter(
dbc.Button("Click to Close",
id='close-single_ols',
className="ml-auto",
style={
"background-color": "#2a8cff",
'width': '30%',
'font-size': 14,
},
),
style={
"background-color": "#696969",
"display": "flex",
"justify-content": "center",
"align-items": "center",
},
),
],
id="modal-single_ols",
is_open=False,
centered=True,
autoFocus=True,
fullscreen=True,
keyboard=True,
fade=True,
),
],
style={'display': 'inline-block',
'width': '45%',
}
),
html.Br(),
html.Br(),
html.Br(),
html.Div(
children=[
html.H5("Quantile regression",
style={'display': 'inline-block',
},
),
dcc.Markdown("Abandon constraints of linear regression and examine the upper " +
"and lower bounds of a 1-to-1 relationship, or any area in " +
"between. The Rush Regression Workbench extends quantile " +
"regression to curvilinear relationships and the use of automated, " +
"optimized forms of data scaling and feature selection.",
style={'width': '94.1%',
},
),
dbc.Button("Run quantile regression",
id='open-quant_reg',
style={
"background-color": "#2a8cff",
'width': '92.5%',
'font-size': 12,
'display': 'inline-block',
},
),
dbc.Modal([
dbc.ModalBody([
html.Div(
id="left-column3_quant",
className="two columns",
children=[quantile_regression.control_card_quantile_regression(),
quantile_regression.generate_figure_quantile_regression(),
],
style={'width': '95.3%',
'border-radius': '15px',
'box-shadow': '1px 1px 1px grey',
'background-color': '#f0f0f0',
'padding': '10px',
'margin-bottom': '10px',
},
),
],
),
dbc.ModalFooter(
dbc.Button("Click to Close",
id='close-quant_reg',
className="ml-auto",
style={
"background-color": "#2a8cff",
'width': '30%',
'font-size': 14,
},
),
style={
"background-color": "#696969",
"display": "flex",
"justify-content": "center",
"align-items": "center",
},
),
],
id="modal-quant_reg",
is_open=False,
centered=True,
autoFocus=True,
fullscreen=True,
keyboard=True,
fade=True,
),
],
style={'display': 'inline-block',
'width': '45%',
'margin-right': '7%',
'margin-left': '1%',
},
),
html.Div(
children=[
html.H5("Multivariable Forms of Linear Regression",
style={'display': 'inline-block',
},
),
dcc.Markdown("Understand how multiple predictors collectively influence " +
"another variable. Take the OLS approach or use **Ridge " +
"regression** and **Lasso regression** to reduce statistical " +
"issues that arise when using many predictors. Automated, " +
"optimized forms of data scaling and feature selection are included.",
style={'width': '94.1%',
},
),
dbc.Button("Run multivariable forms of linear regression",
id='open-multi_reg',
style={
"background-color": "#2a8cff",
'width': '92.5%',
'font-size': 12,
'display': 'inline-block',
},
),
dbc.Modal([
dbc.ModalBody([
html.Div(
id="left-column4",
className="two columns",
children=[MLR.control_card_linear_multivariable(),
MLR.generate_linear_multivariable_outputs(),
],
style={'width': '95.3%',
'display': 'block',
'border-radius': '15px',
'box-shadow': '1px 1px 1px grey',
'background-color': '#f0f0f0',
'padding': '10px',
'margin-bottom': '10px',
},
),
],
),
dbc.ModalFooter(
dbc.Button("Click to Close",
id='close-multi_reg',
className="ml-auto",
style={
"background-color": "#2a8cff",
'width': '30%',
'font-size': 14,
},
),
style={
"background-color": "#696969",
"display": "flex",
"justify-content": "center",
"align-items": "center",
},
),
],
id="modal-multi_reg",
is_open=False,
centered=True,
autoFocus=True,
fullscreen=True,
keyboard=True,
fade=True,
),
],
style={'display': 'inline-block',
'width': '45%',
},
),
html.Br(),
html.Br(),
html.Br(),
html.Div(
children=[
html.H5("Binary Classification",
style={'display': 'inline-block',
},
),
dcc.Markdown("Use **logistic regression** and **probit regression** to " +
"classify outcomes (e.g., readmission/non-readmission, " +
"positive/negative) based on one or more predictor variables. " +
"Leverage machine learning, automated optimized data scaling, " +
"and diagostic curves.",