ml-lib / codelib Goto Github PK

Is your feature request related to a problem? Please describe.

Time series method to predict the next n intervals.

Describe the solution you'd like

Add a time series method for building forecasting models with/without exogenous variables using XGBoost.

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict

Expected output(s)

predict

Additional context

No response

Acceptance criteria

Integration tests:

• Ensure that predictions is not flat after a certain intervals.

Version

v0.4.0

Is your feature request related to a problem? Please describe.

GLMNet module.

Describe the solution you'd like

GLMNet module for building models. Determine optimal alpha and lambda.

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
param: Dict

Expected output(s)

fit
predict

Additional context

No response

Acceptance criteria

Integration tests

• Compare betas to a known equation.

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Time series method to predict the next n intervals.

Describe the solution you'd like

Add a time series method for building forecasting models with/without exogenous variables using Random forest.

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict

Expected output(s)

predict

Additional context

No response

Acceptance criteria

Integration tests:

• Ensure that predictions is not flat after a certain intervals.

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Traveling salesman problem.

Describe the solution you'd like

Travelling salesman problem with both integer programming and heuristic.

Methods:

1. Optimization
2. Heuristic

Expected input(s)

df: pandas.DataFrame
method: Union[str]
param: Dict

Expected output(s)

solve

Additional context

• Document the formulations.

Acceptance criteria

• Integration tests

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Time series module to predict the next n intervals.

Describe the solution you'd like

Time series module for building forecasting models with/without exogenous variables using SARIMAX.

Expected input(s)

df: pandas.DataFrame
y_var: str
x_var: List[str]
params: Dict

Expected output(s)

model: object
model_summary: Dict

Additional context

Expected API:

mod = AutoArima(df=df_ip,
                y_var="y",
                x_var=["cost", "stock_level", "retail_price"])

df_op = mod.predict(x_predict)

Acceptance criteria

• Integration tests

Version

v0.4.0

Describe the maintenance request

Add a contributor code of conduct in the next available release.

Current impact

We are missing a Code of conduct for the repository which is the only piece missing for the community profile.

Version

v0.4.0

Additional context

No response

Is your feature request related to a problem? Please describe.

XGBoost module.

Describe the solution you'd like

XGBoost module for both classification and regression. Determine optimal hyperparameters

Methods:

1. Classification
2. Regression

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict

Expected output(s)

fit
predict

Additional context

No response

Acceptance criteria

• Integration tests

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Time series method to predict the next n intervals.

Describe the solution you'd like

Add a time series method for building forecasting models with/without exogenous variables using fbprophet.

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
param: Dict

Expected output(s)

predict

Additional context

No response

Acceptance criteria

• Integration tests

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Random forest module.

Describe the solution you'd like

Random forest module for both classification and regression. Determine optimal hyperparameters

Methods:

1. Classification
2. Regression

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict

Expected output(s)

fit
predict

Additional context

No response

Acceptance criteria

• Integration tests

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Implement Work force planning for House construction from attached PDF

Describe the solution you'd like

Build a module to implement constraint programming using CP optimizer

Expected input(s)

Input data

Expected output(s)

Output data

Additional context

No response

Acceptance criteria

Generate workforce planning solution

Version

v0.5.0 (Default)

Is your feature request related to a problem? Please describe.

Transportation problem.

Describe the solution you'd like

Transportation problem using integer programming.

Expected input(s)

df: pandas.DataFrame
method: Union[str]
param: Dict

Expected output(s)

solve

Additional context

No response

Acceptance criteria

• Integration tests

Version

v0.4.0

Is your feature request related to a problem? Please describe.

A clustering module to cluster any given data (categorical/continuos/ordinal) and returns optimal clustering solution.

Describe the solution you'd like

Compute optimal clustering solution using gap-statistic.

Methods:

1. First SE
2. Maximum Gap

Expected input(s)

df: pandas.DataFrame
x_var: List[str]
max_cluster: int
method: Union[str]

Expected output(s)

opt_k

Additional context

No response

Acceptance criteria

Integration tests:

• Categorical variables only
• Continuos variables only
• Ordinal variables only
• Combination of categorical/ordinal/continuos

Version

v0.4.0

Is your feature request related to a problem? Please describe.

kNN module.

Describe the solution you'd like

kNN module for both classification and regression. Determine optimal k.

Methods:

1. Classification
2. Regression

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict

Expected output(s)

fit
predict

Additional context

No response

Acceptance criteria

• Integration tests

Version

v0.4.0

Describe the maintenance request

Update README for module changes and add projects view.

Current impact

Incorrect labels.

Version

v0.4.0

Additional context

No response

Is your feature request related to a problem? Please describe.

General additive model framework to predict the next n intervals.

Describe the solution you'd like

Add a GAM based time series method for building forecasting models.

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict

Expected output(s)

predict

Additional context

No response

Acceptance criteria

Integration tests

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Time series method to predict the next n intervals.

Describe the solution you'd like

Add a time series method for building forecasting models with/without exogenous variables using GLMNet.

Expected input(s)

df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict

Expected output(s)

predict

Additional context

No response

Acceptance criteria

Integration tests

Version

v0.4.0

Is your feature request related to a problem? Please describe.

Ensemble forecasting model.

Describe the solution you'd like

Bates & Granger approach to combine forecasts to predict the next n intervals.

Expected input(s)

df: pandas.DataFrame
y: str
y_hat: List[str]
pred_period: int
lag: int

Expected output(s)

model: object
weights: Dict

Additional context

Expected API:

mod = BatesGrager(df=df_ip,
                  y="y",
                  y_hat=["y_hat_01", "y_hat_02",
                         "y_hat_03", "y_hat_04"],
                  lag=53, pred_period=1)
df_op = mod.solve()

Acceptance criteria

• Integration tests

Version

v0.4.1 (Default)