prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Construct comperhensive test suite to evaluate predictions with missing column
  We should have a test suite to evaluate prediction accuracy with missing column.
  
  This should take the form of:
  
  Given `M` columns and a Lightwood model trained with them to predict `y`, the accuracy for `y` when predicting with `M` columns (where `M` is a subset of `N`), should be about equal to or greater than that of a Gradient Boosting Regressor or Classifier trained with just the columns `M` to predict `y`.
  
  The reason we are using a Gradient Booster to determine the benchmark accuracy is that it's safe to assume they are fairly generic (i.e. should get about the same accuracy as a well trained neural network) and fast&easy to train.
  
  We can do this testing in two phases:
  
  First, we can add this as a check to the generate-data tests in lightwood, which should be fairly easy.
  
  Second, we can add these tests to mindsdb_examples, the helpers that are already present in there can help.
  
  I'll be handling this but @torrmal feel free to review the methodology
  
  </issue>
  <code>
  [start of docs/examples/learn_to_classify.py]
  1 import lightwood
  2 import random
  3 import pandas as pd
  4 import numpy as np
  5 from collections import Counter
  6 
  7 
  8 random.seed(66)
  9 n = 100
  10 m = 500
  11 train = True
  12 nr_inputs = 10
  13 
  14 #options = ['a','b','c','d','e','f','g','h','n','m']
  15 options = ['a','b','c']
  16 
  17 data_train = {}
  18 data_test = {}
  19 
  20 for data, nr_ele in [(data_train,n), (data_test,m)]:
  21     for i in range(nr_inputs):
  22         data[f'x_{i}'] = [random.choice(options) for _ in range(nr_ele)]
  23 
  24     data['y'] = [Counter([data[f'x_{i}'][n] for i in range(nr_inputs)]).most_common(1)[0][0] for n in range(nr_ele)]
  25 
  26 data_train = pd.DataFrame(data_train)
  27 data_test = pd.DataFrame(data_test)
  28 
  29 def iter_function(epoch, training_error, test_error, test_error_gradient, test_accuracy):
  30     print(f'Epoch: {epoch}, Train Error: {training_error}, Test Error: {test_error}, Test Error Gradient: {test_error_gradient}, Test Accuracy: {test_accuracy}')
  31 
  32 if train:
  33     predictor = lightwood.Predictor(output=['y'])
  34     predictor.learn(from_data=data_train, callback_on_iter=iter_function, eval_every_x_epochs=200)
  35     predictor.save('/tmp/ltcrl.pkl')
  36 
  37 predictor = lightwood.Predictor(load_from_path='/tmp/ltcrl.pkl')
  38 print('Train accuracy: ', predictor.train_accuracy['y']['value'])
  39 print('Test accuracy: ', predictor.calculate_accuracy(from_data=data_test)['y']['value'])
  40 
  41 predictions = predictor.predict(when_data=data_test)
  42 print(f'Confidence mean for all columns present ', np.mean(predictions['y']['selfaware_confidences']))
  43 
  44 for i_drop in range(nr_inputs):
  45     predictions = predictor.predict(when_data=data_test.drop(columns=[f'x_{i_drop}']))
  46     print(f'Accuracy for x_{i_drop} missing: ', predictor.calculate_accuracy(from_data=data_test.drop(columns=[f'x_{i_drop}']))['y']['value'])
  47     print(f'Confidence mean for x_{i_drop} missing: ', np.mean(predictions['y']['selfaware_confidences']))
  48
  [end of docs/examples/learn_to_classify.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/docs/examples/learn_to_classify.py b/docs/examples/learn_to_classify.py
  --- a/docs/examples/learn_to_classify.py
  +++ b/docs/examples/learn_to_classify.py
  @@ -34,14 +34,18 @@
       predictor.learn(from_data=data_train, callback_on_iter=iter_function, eval_every_x_epochs=200)
       predictor.save('/tmp/ltcrl.pkl')
   
  +
   predictor = lightwood.Predictor(load_from_path='/tmp/ltcrl.pkl')
   print('Train accuracy: ', predictor.train_accuracy['y']['value'])
   print('Test accuracy: ', predictor.calculate_accuracy(from_data=data_test)['y']['value'])
   
  -predictions = predictor.predict(when_data=data_test)
  +print(f'Accuracy for all columns present: ', predictor.calculate_accuracy(from_data=data_test)['y']['value'])
  +
  +predictions = predictor.calculate_accuracy(from_data=data_test)
   print(f'Confidence mean for all columns present ', np.mean(predictions['y']['selfaware_confidences']))
   
   for i_drop in range(nr_inputs):
  -    predictions = predictor.predict(when_data=data_test.drop(columns=[f'x_{i_drop}']))
       print(f'Accuracy for x_{i_drop} missing: ', predictor.calculate_accuracy(from_data=data_test.drop(columns=[f'x_{i_drop}']))['y']['value'])
  +
  +    predictions = predictor.calculate_accuracy(from_data=data_test.drop(columns=[f'x_{i_drop}']))
       print(f'Confidence mean for x_{i_drop} missing: ', np.mean(predictions['y']['selfaware_confidences']))
  
verification_info: {"golden_diff": "diff --git a/docs/examples/learn_to_classify.py b/docs/examples/learn_to_classify.py\n--- a/docs/examples/learn_to_classify.py\n+++ b/docs/examples/learn_to_classify.py\n@@ -34,14 +34,18 @@\n     predictor.learn(from_data=data_train, callback_on_iter=iter_function, eval_every_x_epochs=200)\n     predictor.save('/tmp/ltcrl.pkl')\n \n+\n predictor = lightwood.Predictor(load_from_path='/tmp/ltcrl.pkl')\n print('Train accuracy: ', predictor.train_accuracy['y']['value'])\n print('Test accuracy: ', predictor.calculate_accuracy(from_data=data_test)['y']['value'])\n \n-predictions = predictor.predict(when_data=data_test)\n+print(f'Accuracy for all columns present: ', predictor.calculate_accuracy(from_data=data_test)['y']['value'])\n+\n+predictions = predictor.calculate_accuracy(from_data=data_test)\n print(f'Confidence mean for all columns present ', np.mean(predictions['y']['selfaware_confidences']))\n \n for i_drop in range(nr_inputs):\n-    predictions = predictor.predict(when_data=data_test.drop(columns=[f'x_{i_drop}']))\n     print(f'Accuracy for x_{i_drop} missing: ', predictor.calculate_accuracy(from_data=data_test.drop(columns=[f'x_{i_drop}']))['y']['value'])\n+\n+    predictions = predictor.calculate_accuracy(from_data=data_test.drop(columns=[f'x_{i_drop}']))\n     print(f'Confidence mean for x_{i_drop} missing: ', np.mean(predictions['y']['selfaware_confidences']))\n", "issue": "Construct comperhensive test suite to evaluate predictions with missing column\nWe should have a test suite to evaluate prediction accuracy with missing column.\r\n\r\nThis should take the form of:\r\n\r\nGiven `M` columns and a Lightwood model trained with them to predict `y`, the accuracy for `y` when predicting with `M` columns (where `M` is a subset of `N`), should be about equal to or greater than that of a Gradient Boosting Regressor or Classifier trained with just the columns `M` to predict `y`.\r\n\r\nThe reason we are using a Gradient Booster to determine the benchmark accuracy is that it's safe to assume they are fairly generic (i.e. should get about the same accuracy as a well trained neural network) and fast&easy to train.\r\n\r\nWe can do this testing in two phases:\r\n\r\nFirst, we can add this as a check to the generate-data tests in lightwood, which should be fairly easy.\r\n\r\nSecond, we can add these tests to mindsdb_examples, the helpers that are already present in there can help.\r\n\r\nI'll be handling this but @torrmal feel free to review the methodology\n", "before_files": [{"content": "import lightwood\nimport random\nimport pandas as pd\nimport numpy as np\nfrom collections import Counter\n\n\nrandom.seed(66)\nn = 100\nm = 500\ntrain = True\nnr_inputs = 10\n\n#options = ['a','b','c','d','e','f','g','h','n','m']\noptions = ['a','b','c']\n\ndata_train = {}\ndata_test = {}\n\nfor data, nr_ele in [(data_train,n), (data_test,m)]:\n    for i in range(nr_inputs):\n        data[f'x_{i}'] = [random.choice(options) for _ in range(nr_ele)]\n\n    data['y'] = [Counter([data[f'x_{i}'][n] for i in range(nr_inputs)]).most_common(1)[0][0] for n in range(nr_ele)]\n\ndata_train = pd.DataFrame(data_train)\ndata_test = pd.DataFrame(data_test)\n\ndef iter_function(epoch, training_error, test_error, test_error_gradient, test_accuracy):\n    print(f'Epoch: {epoch}, Train Error: {training_error}, Test Error: {test_error}, Test Error Gradient: {test_error_gradient}, Test Accuracy: {test_accuracy}')\n\nif train:\n    predictor = lightwood.Predictor(output=['y'])\n    predictor.learn(from_data=data_train, callback_on_iter=iter_function, eval_every_x_epochs=200)\n    predictor.save('/tmp/ltcrl.pkl')\n\npredictor = lightwood.Predictor(load_from_path='/tmp/ltcrl.pkl')\nprint('Train accuracy: ', predictor.train_accuracy['y']['value'])\nprint('Test accuracy: ', predictor.calculate_accuracy(from_data=data_test)['y']['value'])\n\npredictions = predictor.predict(when_data=data_test)\nprint(f'Confidence mean for all columns present ', np.mean(predictions['y']['selfaware_confidences']))\n\nfor i_drop in range(nr_inputs):\n    predictions = predictor.predict(when_data=data_test.drop(columns=[f'x_{i_drop}']))\n    print(f'Accuracy for x_{i_drop} missing: ', predictor.calculate_accuracy(from_data=data_test.drop(columns=[f'x_{i_drop}']))['y']['value'])\n    print(f'Confidence mean for x_{i_drop} missing: ', np.mean(predictions['y']['selfaware_confidences']))\n", "path": "docs/examples/learn_to_classify.py"}]}