andrewm4894

package org.datavec.transform.basic;

import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.datavec.api.transform.TransformProcess;
import org.datavec.api.transform.schema.Schema;
import org.datavec.api.transform.transform.sequence.SequenceOffsetTransform;
import org.datavec.api.writable.Writable;
import org.datavec.local.transforms.LocalTransformExecutor;

andrewm4894

import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.datavec.api.transform.TransformProcess;
import org.datavec.api.transform.schema.Schema;
import org.datavec.api.writable.Writable;
import org.datavec.local.transforms.LocalTransformExecutor;
import org.nd4j.linalg.io.ClassPathResource;

import java.io.File;

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import org.kohsuke.args4j.CmdLineParser;
import org.kohsuke.args4j.Option;

/**
 * Hello world! class that is paramaterized (with defaults) using arg4j.
 * Example cli usage: java -jar helloWorldParamaterized --msg='Hello arg4j!'
 */
public class helloWorldParamaterized 
{

andrewm4894

import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Add;

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

public class wekaDev {

andrewm4894

{
	"config_bank": {
		"notebook": "notebooks/data_explorer.ipynb",
		"data_url": "https://raw.githubusercontent.com/andrewm4894/papermill_dev/master/data/bank-full.csv",
		"output_label": "bank"
	},
	"config_adult": {
		"notebook": "notebooks/data_explorer.ipynb",
		"data_url": "https://raw.githubusercontent.com/andrewm4894/papermill_dev/master/data/adult.csv",
		"output_label": "adult"

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import papermill as pm
import multiprocessing
import os
import argparse
import json


def run_papermill(config):
    ''' Function to run notebook(s) in paralell using papermill.
    '''

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def plot_lines_multi(df,lw=2,pw=700,ph=400,t_str="hover,save,pan,box_zoom,reset,wheel_zoom",t_loc='above'):
    '''...
    '''
    source = ColumnDataSource(df)
    col_names = source.column_names
    p = figure(x_axis_type="datetime",plot_width=pw, plot_height=ph,toolbar_location=t_loc, tools=t_str)
    p_dict = dict()
    for col, c, col_name in zip(df.columns,color,col_names):
        p_dict[col_name] = p.line('index', col, source=source, color=c,line_width=lw)
        p.add_tools(HoverTool(

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import pandas as pd
import numpy as np
import random 
import string

def make_data(start_date='2019-01-01',n_data=30,n_num_var=5,n_cat_var=5,n_cat_var_cardinality_upper=10):
    ''' Function to make some data and put it in a df
    '''
    dates = pd.date_range(start_date,periods=n_data)
    df = pd.DataFrame()

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PS C:\Users\amaguire\Documents\GitHub\ami-research\ami> pipenv install jupyterlab
Installing jupyterlab…
Adding jupyterlab to Pipfile's [packages]…
Installation Succeeded
Pipfile.lock (adb84f) out of date, updating to (ca72e7)…
Locking [dev-packages] dependencies…
Locking [packages] dependencies…
Success!
Updated Pipfile.lock (adb84f)!
Installing dependencies from Pipfile.lock (adb84f)…

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# now train on new data
print(f'... reshaping data for new data training ...')
data_train_new = data_reshape_for_model(data_new,N_TIMESTEPS,N_FEATURES)
print("... begin training on new data ...")
model = train(model,data_train_new,n_epochs=1)
yhat_new = predict(model,data_train_new)

df_out_new = yhat_to_df_out(data_train_new,yhat_new,N_TIMESTEPS,N_FEATURES)

plot_cols = [col for col in df_out_new.columns if 'error_avg' in col]