Model results drastically changing when running same lines as script or as function

Weird thing happening today. I'm running a RandomForestRegressor over 8 categories. First, I tried looping a function graphing y_test and my prediction, but then I noticed that those graphs were inconsistent with my results elsewhere in the script, so I tried running turning the function into a simple script. The function plus loop looks like this:

def plotModelResults(model, X_train=X_train, X_test=X_test, plot_intervals=False, plot_anomalies=False):



    model.fit(X_train.values,y_train.values.ravel())

    prediction = model.predict(X_test.values)

    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)

Nothing too fancy. And it's looped like this:

nplots=df['Equipos'].nunique()

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )

dicc=dict()

for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)



    plt.tight_layout()



    plotModelResults(RandomForestRegressor(n_estimators=100, random_state=42))

The first graph from this script looks like this:

Graph 1

And then, I run this:

nplots=len(df['Equipos'].unique())

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )



for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Fecha_Venta','Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)

    rf=RandomForestRegressor(n_estimators=100, random_state=42)

        #Fiteamos modelo

    rf.fit(X_train.values,y_train.values.ravel())



    prediction = rf.predict(X_test.values)





    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    #Ploteamos valores de test

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)



    plt.tight_layout()

From which its first graph looks like this:

Graph 2

As far as I can see, both scripts are equivalent, so I have no idea why are those graphs different. Any ideas?

edited Nov 22 '18 at 21:29

asked Nov 22 '18 at 20:40

Juan C

30411

1) Both the script and the function don't do exactly the same thing. Try calling plotModelResults within your script. 2) are you running this in a jupyter notebook? It's possible that you're keeping state somewhere on accident and that's causing strange results
– Ian Quah
Nov 22 '18 at 21:33

add a comment |

def plotModelResults(model, X_train=X_train, X_test=X_test, plot_intervals=False, plot_anomalies=False):



    model.fit(X_train.values,y_train.values.ravel())

    prediction = model.predict(X_test.values)

    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)

Nothing too fancy. And it's looped like this:

nplots=df['Equipos'].nunique()

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )

dicc=dict()

for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)



    plt.tight_layout()



    plotModelResults(RandomForestRegressor(n_estimators=100, random_state=42))

The first graph from this script looks like this:

Graph 1

And then, I run this:

nplots=len(df['Equipos'].unique())

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )



for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Fecha_Venta','Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)

    rf=RandomForestRegressor(n_estimators=100, random_state=42)

        #Fiteamos modelo

    rf.fit(X_train.values,y_train.values.ravel())



    prediction = rf.predict(X_test.values)





    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    #Ploteamos valores de test

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)



    plt.tight_layout()

From which its first graph looks like this:

Graph 2

As far as I can see, both scripts are equivalent, so I have no idea why are those graphs different. Any ideas?

edited Nov 22 '18 at 21:29

asked Nov 22 '18 at 20:40

Juan C

30411

1) Both the script and the function don't do exactly the same thing. Try calling plotModelResults within your script. 2) are you running this in a jupyter notebook? It's possible that you're keeping state somewhere on accident and that's causing strange results
– Ian Quah
Nov 22 '18 at 21:33

add a comment |

def plotModelResults(model, X_train=X_train, X_test=X_test, plot_intervals=False, plot_anomalies=False):



    model.fit(X_train.values,y_train.values.ravel())

    prediction = model.predict(X_test.values)

    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)

Nothing too fancy. And it's looped like this:

nplots=df['Equipos'].nunique()

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )

dicc=dict()

for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)



    plt.tight_layout()



    plotModelResults(RandomForestRegressor(n_estimators=100, random_state=42))

The first graph from this script looks like this:

Graph 1

And then, I run this:

nplots=len(df['Equipos'].unique())

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )



for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Fecha_Venta','Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)

    rf=RandomForestRegressor(n_estimators=100, random_state=42)

        #Fiteamos modelo

    rf.fit(X_train.values,y_train.values.ravel())



    prediction = rf.predict(X_test.values)





    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    #Ploteamos valores de test

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)



    plt.tight_layout()

From which its first graph looks like this:

Graph 2

As far as I can see, both scripts are equivalent, so I have no idea why are those graphs different. Any ideas?

edited Nov 22 '18 at 21:29

asked Nov 22 '18 at 20:40

Juan C

30411

def plotModelResults(model, X_train=X_train, X_test=X_test, plot_intervals=False, plot_anomalies=False):



    model.fit(X_train.values,y_train.values.ravel())

    prediction = model.predict(X_test.values)

    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)

Nothing too fancy. And it's looped like this:

nplots=df['Equipos'].nunique()

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )

dicc=dict()

for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)



    plt.tight_layout()



    plotModelResults(RandomForestRegressor(n_estimators=100, random_state=42))

The first graph from this script looks like this:

Graph 1

And then, I run this:

nplots=len(df['Equipos'].unique())

f, axs = plt.subplots(figsize=(25,5),nrows=1, ncols=nplots, sharey=True )



for i,team in enumerate(df['Equipos'].unique()):





    aux=df.loc[df['Equipos']==team]

    X=aux.drop(columns=['Fecha_Venta','Cantidad_Vendida','Equipos']).copy()

    y=aux[['Cantidad_Vendida']].copy()

    X_train, X_test, y_train, y_test = timeseries_train_test_split(X, y, test_size=0.1)

    rf=RandomForestRegressor(n_estimators=100, random_state=42)

        #Fiteamos modelo

    rf.fit(X_train.values,y_train.values.ravel())



    prediction = rf.predict(X_test.values)





    axs[i].plot(prediction, "g", label="prediction", linewidth=2.0)

    #Ploteamos valores de test

    axs[i].plot(y_test.values.ravel(), label="actual", linewidth=2.0)

    axs[i].legend(loc="best")

    axs[i].set_title('{}'.format(team))

    axs[i].grid(True)



    plt.tight_layout()

From which its first graph looks like this:

Graph 2

As far as I can see, both scripts are equivalent, so I have no idea why are those graphs different. Any ideas?

python pandas scikit-learn

edited Nov 22 '18 at 21:29

asked Nov 22 '18 at 20:40

Juan C

30411

edited Nov 22 '18 at 21:29

asked Nov 22 '18 at 20:40

Juan C

30411

edited Nov 22 '18 at 21:29

asked Nov 22 '18 at 20:40

Juan C

30411

asked Nov 22 '18 at 20:40

Juan C

30411

asked Nov 22 '18 at 20:40

Juan C

30411

1) Both the script and the function don't do exactly the same thing. Try calling plotModelResults within your script. 2) are you running this in a jupyter notebook? It's possible that you're keeping state somewhere on accident and that's causing strange results
– Ian Quah
Nov 22 '18 at 21:33

add a comment |

1) Both the script and the function don't do exactly the same thing. Try calling plotModelResults within your script. 2) are you running this in a jupyter notebook? It's possible that you're keeping state somewhere on accident and that's causing strange results
– Ian Quah
Nov 22 '18 at 21:33

1) Both the script and the function don't do exactly the same thing. Try calling plotModelResults within your script. 2) are you running this in a jupyter notebook? It's possible that you're keeping state somewhere on accident and that's causing strange results
– Ian Quah
Nov 22 '18 at 21:33

add a comment |

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