Grasshopper to CSV file using a SVM example

Steps followed in this notebook:

 

1. Import CSV file

2. Selecting features and labels

3. ML or data analysis

4. Export results to CSV

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You can download the notebook version here:

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You can download the Grasshopper script using the link here:

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Gh to CSV using a SVM example - Gh script

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Code

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import numpy as np

import pandas as pd

import os

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Import CSV file

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# Your CSV file name here

csv_file_name = "test file.csv"

 

current_path = os.getcwd()

csv_filepath = os.path.join(current_path, csv_file_name)

 

# print(csv_filepath)

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# Option 1: Import using Numpy

my_data_numpy = np.genfromtxt(csv_filepath, delimiter=',')

print(my_data_numpy[0:5])

print('\n \t Without first row of empty labels \n')

print(my_data_numpy[1:5])

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[[     nan      nan     nan      nan]

 [0.542188 0.452487 0.      0.      ]

 [0.808325 0.634651 0.      0.      ]

 [1.331997 0.536045 0.      0.      ]

 [3.970094 0.116322 0.      0.      ]]

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   Without first row of empty labels

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[[0.542188 0.452487 0.      0.      ]

 [0.808325 0.634651 0.      0.      ]

 [1.331997 0.536045 0.      0.      ]

 [3.970094 0.116322 0.      0.      ]]

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# Option 2: Import using Pandas (preferred)

my_data_pandas = pd.read_csv(csv_filepath)

print(my_data_pandas[0:5])

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         x         y z is it inside?

0 0.542188  0.452487 0             0

1 0.808325  0.634651 0             0

2 1.331997  0.536045 0             0

3 3.970094  0.116322 0             0

4 3.218009 -0.587934 0             0

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# Pandas also has its own display, which appears when not using print()

my_data_pandas.head(5)

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Selecting features and labels

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# Here you can select your features from the pandas dataframe, and also select the labels for prediction

features = list(zip(my_data_pandas['x'], my_data_pandas['y']))

 

prediction_labels = list(my_data_pandas['is it inside?']) # convert to a simple list for sklearn

 

print(features[0:5])

print(prediction_labels[0:5])

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[(0.542188, 0.452487), (0.808325, 0.634651), (1.331997, 0.536045), (3.970094, 0.116322), (3.218009, -0.587934)] [0, 0, 0, 0, 0]

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from sklearn.model_selection import train_test_split

 

# Split dataset into training set and test set

X_train, X_test, y_train, y_test = train_test_split(features, prediction_labels, test_size=0.3) # 70% training and 30% test

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ML or data analysis

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# ML input here...

 

# SVM example

 

from sklearn import svm

 

clf = svm.SVC(kernel='rbf', C=1000)  # note: try rbf and C = 1000

clf.fit(X_train, y_train)

 

y_pred = clf.predict(X_test)

 

from sklearn.metrics import accuracy_score

 

print('Accuracy is currently: ', accuracy_score(y_test, y_pred))

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Accuracy is currently: 0.9946666666666667

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my_data_pandas['new predicted values'] = clf.predict(features)

print(my_data_pandas['is it inside?'].value_counts())

print(my_data_pandas['new predicted values'].value_counts())

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0 1987

1  513

Name: is it inside?, dtype: int64

0 1991

1 509

Name: new predicted values, dtype: int64

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Export Results to CSV

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# Results export

df_out = my_data_pandas # edit output pandas dataframe here

out_suffix = 'python output' # name to put after new file

 

output_file_name = csv_file_name[:-4] + '_' + out_suffix + '.csv' # This takes the original csv name and appends the suffix name. The [:-4] takes the whole name, except the last 4 characters (.csv)

 

print('Your new output file, is in the same folder with the name: ', output_file_name)

 

# The output file path

csv_out_filepath = os.path.join(current_path, output_file_name)

 

# creating csv at file path from the df_out specificed pandas dataframe

df_out.to_csv(csv_out_filepath, index=False)

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Your new output file, is in the same folder with the name: test file_python output.csv