Author: Stephen Situ

This notebook documents the development of a computer vision model that uses a convolutional neural network to quantify methane emissions. The model takes an image as the input feature and the emissions rate as the label. This work was conducted as part of the 2023 Zeroing Methane Emissions Datathon competition.

The first step of the project was to preprocess the data by merging two CSV files into a pandas dataframe and performing data cleaning. Next, we found the file path of every image in the directory. We noticed that the distribution of emissions rates was highly skewed and used the square root transformation to normalize the data and improve the distribution's normality.

We used the standard sci-kit learn split to divide the dataframe into training and validation sets. Using the TensorFlow ImageDataGenerator and flow_from_dataframe object, we normalized the pixel values, resized the images, batched the data, and created an augmented dataset of the training data.

To address high uncertainty in the data, we implemented a custom loss function that penalizes data points with higher uncertainty. We then defined the convolutional neural network with the correct input shape (217, 217, 3) and output shape (1). The output shape of 1 is appropriate for this regression task.

The history object was assigned to the model.fit method, and we converted it into a dataframe to assess our model's performance during training. We also used the model.predict method on other data to make predictions.

In [ ]:
# import libraries
import numpy as np
import pandas as pd
import tensorflow as tf
import random
import imageio
import matplotlib.pyplot as plt
In [48]:
# view random image:
directory_1 = "Pollute"
files = tf.io.gfile.glob(directory_1 + "/*.png")
random_file = random.choice(files)
img = imageio.imread(random_file)
plt.imshow(img) 

C:\Users\Steve\AppData\Local\Temp\ipykernel_15200\1183388691.py:5: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
  img = imageio.imread(random_file)
Out[48]:
<matplotlib.image.AxesImage at 0x1e1d8b31a90>
In [2]:
# Read CSV
df1 = pd.read_csv("permian_plume_list_2019 Jeremy Zhao.csv")
df3 = pd.read_csv("plume_attribution_2019-2022 Jeremy Zhao.csv")
In [3]:
# Union dataframes
df7 = pd.concat([df1[['source_id', 'candidate_id','qplume','sigma_qplume']], df3[['source_id', 'candidate_id','qplume','sigma_qplume']]])
In [4]:
df7
Out[4]:
source_id candidate_id qplume sigma_qplume
0 P00001 ang20190922t192642-2 2579.528100 902.732655
1 P00002 ang20190922t192642-4 420.725207 147.155782
2 P00003 ang20190922t192642-5 1051.909600 339.267587
3 P00004 ang20190922t194340-1 1736.644250 1035.427670
4 P00005 ang20190922t194340-2 870.605019 435.968147
... ... ... ... ...
8747 R0290 GAO20220506t200303p0000-C 4696.416802 2168.145498
8748 R0291 ang20220514t182158-A 3669.390570 1068.296306
8749 R0292 ang20220514t182953-A 72.882097 24.608781
8750 R0292 ang20220514t183955-A 240.217310 89.888867
8751 R0293 ang20220514t190216-A 488.787135 88.267676

11819 rows × 4 columns

In [40]:
# function to find all file names in column in "Pollute" folder
import os
def find_file(candidate_id):
    folder = "Pollute"
    for file in os.listdir(folder):
        if str(candidate_id) in file:
            return file
    return None
In [41]:
# find the file names in directory
df7['file_name'] = df7['candidate_id'].apply(find_file)
In [42]:
# Change file_name to correct directory
df7['file_name'] = 'Pollute/' + df7['file_name'].astype(str)
In [44]:
df7
Out[44]:
source_id candidate_id qplume sigma_qplume file_name
0 P00001 ang20190922t192642-2 2579.528100 902.732655 Pollute/ang20190922t192642-2_r4578_c217-plume.png
1 P00002 ang20190922t192642-4 420.725207 147.155782 Pollute/ang20190922t192642-4_r4928_c373-plume.png
2 P00003 ang20190922t192642-5 1051.909600 339.267587 Pollute/ang20190922t192642-5_r6423_c113-plume.png
3 P00004 ang20190922t194340-1 1736.644250 1035.427670 Pollute/ang20190922t194340-1_r1953_c438-plume.png
4 P00005 ang20190922t194340-2 870.605019 435.968147 Pollute/ang20190922t194340-2_r2642_c324-plume.png
... ... ... ... ... ...
8747 R0290 GAO20220506t200303p0000-C 4696.416802 2168.145498 Pollute/GAO20220506t200303p0000-C_r345_c1650-p...
8748 R0291 ang20220514t182158-A 3669.390570 1068.296306 Pollute/ang20220514t182158-A_r1191_c276-plume.png
8749 R0292 ang20220514t182953-A 72.882097 24.608781 Pollute/ang20220514t182953-A_r1288_c300-plume.png
8750 R0292 ang20220514t183955-A 240.217310 89.888867 Pollute/ang20220514t183955-A_r1294_c278-plume.png
8751 R0293 ang20220514t190216-A 488.787135 88.267676 Pollute/ang20220514t190216-A_r1729_c286-plume.png

11819 rows × 5 columns

In [45]:
# Drop duplicates
df7 = df7.drop_duplicates()
In [46]:
# drop N/A's
df7 = df7.dropna(subset=['qplume'])
df7 = df7.dropna(subset=['sigma_qplume'])
df7 = df7.dropna(subset=['file_name'])
df7 = df7.where(df7['file_name'] != 'Pollute/None')
In [47]:
df7
Out[47]:
source_id candidate_id qplume sigma_qplume file_name
0 P00001 ang20190922t192642-2 2579.528100 902.732655 Pollute/ang20190922t192642-2_r4578_c217-plume.png
1 P00002 ang20190922t192642-4 420.725207 147.155782 Pollute/ang20190922t192642-4_r4928_c373-plume.png
2 P00003 ang20190922t192642-5 1051.909600 339.267587 Pollute/ang20190922t192642-5_r6423_c113-plume.png
3 P00004 ang20190922t194340-1 1736.644250 1035.427670 Pollute/ang20190922t194340-1_r1953_c438-plume.png
4 P00005 ang20190922t194340-2 870.605019 435.968147 Pollute/ang20190922t194340-2_r2642_c324-plume.png
... ... ... ... ... ...
8747 R0290 GAO20220506t200303p0000-C 4696.416802 2168.145498 Pollute/GAO20220506t200303p0000-C_r345_c1650-p...
8748 R0291 ang20220514t182158-A 3669.390570 1068.296306 Pollute/ang20220514t182158-A_r1191_c276-plume.png
8749 R0292 ang20220514t182953-A 72.882097 24.608781 Pollute/ang20220514t182953-A_r1288_c300-plume.png
8750 R0292 ang20220514t183955-A 240.217310 89.888867 Pollute/ang20220514t183955-A_r1294_c278-plume.png
8751 R0293 ang20220514t190216-A 488.787135 88.267676 Pollute/ang20220514t190216-A_r1729_c286-plume.png

8852 rows × 5 columns

In [66]:
df7.to_csv("clean_data.csv", index=False)
In [49]:
#df7 = df7.where(df7['qplume'] <= 1000) 
#df7 = df7.where(df7['sigma_qplume'] <= 200) 
df7 = df7.dropna(subset=['qplume'])
In [50]:
# scatter plot of data
import matplotlib.pyplot as plt
df7 = df7.reset_index()
df7.plot(kind='scatter', x='index', y='qplume')
plt.title('Scatter plot of qplume')
plt.show()
In [51]:
# Original distribution very skewed
column = df7['qplume']
plt.hist(column, bins=50, edgecolor='black', alpha=0.7)
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Histogram of qplume')
plt.show()
In [52]:
# Use natural log to transform data
df7['log(qplume)'] = np.log(df7['qplume'])
df7['log(sigma_qplume)'] = np.log(df7['sigma_qplume'])
df7['sqrt(qplume)'] = np.sqrt(df7['qplume'])
df7['sqrt(sigma qplume)'] = np.sqrt(df7['sigma_qplume'])
In [53]:
df7
Out[53]:
index source_id candidate_id qplume sigma_qplume file_name log(qplume) log(sigma_qplume) sqrt(qplume) sqrt(sigma qplume)
0 0 P00001 ang20190922t192642-2 2579.528100 902.732655 Pollute/ang20190922t192642-2_r4578_c217-plume.png 7.855362 6.805426 50.789055 30.045510
1 1 P00002 ang20190922t192642-4 420.725207 147.155782 Pollute/ang20190922t192642-4_r4928_c373-plume.png 6.041980 4.991492 20.511587 12.130778
2 2 P00003 ang20190922t192642-5 1051.909600 339.267587 Pollute/ang20190922t192642-5_r6423_c113-plume.png 6.958362 5.826789 32.433156 18.419218
3 3 P00004 ang20190922t194340-1 1736.644250 1035.427670 Pollute/ang20190922t194340-1_r1953_c438-plume.png 7.459710 6.942570 41.673064 32.178062
4 4 P00005 ang20190922t194340-2 870.605019 435.968147 Pollute/ang20190922t194340-2_r2642_c324-plume.png 6.769188 6.077569 29.506017 20.879850
... ... ... ... ... ... ... ... ... ... ...
7680 8747 R0290 GAO20220506t200303p0000-C 4696.416802 2168.145498 Pollute/GAO20220506t200303p0000-C_r345_c1650-p... 8.454555 7.681627 68.530408 46.563349
7681 8748 R0291 ang20220514t182158-A 3669.390570 1068.296306 Pollute/ang20220514t182158-A_r1191_c276-plume.png 8.207781 6.973820 60.575495 32.684802
7682 8749 R0292 ang20220514t182953-A 72.882097 24.608781 Pollute/ang20220514t182953-A_r1288_c300-plume.png 4.288843 3.203103 8.537101 4.960724
7683 8750 R0292 ang20220514t183955-A 240.217310 89.888867 Pollute/ang20220514t183955-A_r1294_c278-plume.png 5.481544 4.498574 15.498945 9.480974
7684 8751 R0293 ang20220514t190216-A 488.787135 88.267676 Pollute/ang20220514t190216-A_r1729_c286-plume.png 6.191927 4.480374 22.108531 9.395088

7685 rows × 10 columns

In [19]:
# Data is much more normal after natural log
column = df7['log(qplume)']
plt.hist(column, bins=50, edgecolor='black', alpha=0.7)
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Histogram of log qplume')
plt.show()
In [54]:
# Histogram of log sigma_qplume
column = df7['sqrt(qplume)']
plt.hist(column, bins=50, edgecolor='black', alpha=0.7)
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Histogram of sqrt qplume')
plt.show()
In [55]:
# train, validation split, 80/20
from sklearn.model_selection import train_test_split
df_train, df_val = train_test_split(df7, test_size=0.20, random_state=42)
In [56]:
df_train
Out[56]:
index source_id candidate_id qplume sigma_qplume file_name log(qplume) log(sigma_qplume) sqrt(qplume) sqrt(sigma qplume)
3204 137 B0021 GAO20210513t143909p0000-2 950.098748 364.227858 Pollute/GAO20210513t143909p0000-2_r10902_c645-... 6.856566 5.897780 30.823672 19.084755
1536 1536 P00657 ang20191016t193637-A 345.038751 56.329304 Pollute/ang20191016t193637-A_r1734_c303-plume.png 5.843657 4.031215 18.575219 7.505285
3668 601 E0125 GAO20210727t164251p0000-G 95.148504 24.316932 Pollute/GAO20210727t164251p0000-G_r5679_c4517-... 4.555439 3.191173 9.754409 4.931220
6202 5239 P00595 ang20191006t150453-A 256.839645 137.028738 Pollute/ang20191006t150453-A_r2196_c261-plume.png 5.548452 4.920191 16.026217 11.705927
501 501 P00354 ang20191018t141549-7 5656.608210 2753.582790 Pollute/ang20191018t141549-7_r3900_c244-plume.png 8.640580 7.920658 75.210426 52.474592
... ... ... ... ... ... ... ... ... ... ...
5226 2160 G0084 GAO20201120t182953p0000-2 1416.972030 424.008456 Pollute/GAO20201120t182953p0000-2_r925_c923-pl... 7.256278 6.049753 37.642689 20.591466
5390 2352 H0062 ang20200828t185516-D 201.554766 61.222435 Pollute/ang20200828t185516-D_r5322_c130-plume.png 5.306061 4.114514 14.196998 7.824477
860 860 P00133 ang20190926t184029-F 108.180459 15.462490 Pollute/ang20190926t184029-F_r10180_c41-plume.png 4.683801 2.738417 10.400984 3.932237
7603 8670 R0280 GAO20220506t173302p0000-D 1193.774724 499.722576 Pollute/GAO20220506t173302p0000-D_r1132_c674-p... 7.084876 6.214053 34.551045 22.354476
7270 8164 R0266 ang20220501t160258-E 468.575482 114.718056 Pollute/ang20220501t160258-E_r12943_c545-plume... 6.149697 4.742477 21.646604 10.710651

6148 rows × 10 columns

In [57]:
# Create numpy array of uncertainty for training data
uncertainty = df_train['sqrt(sigma qplume)'].values
In [58]:
# define a custom loss function
# This loss function takes in three arguments: y_true, which is the true target value, y_pred, 
# which is the predicted target value, and uncertainty, which is the uncertainty associated with the prediction.
# The function first computes the squared error between y_true and y_pred, and then weights the squared error using 
# the exponential of negative twice the uncertainty. It then computes the mean of the weighted squared error and returns 
# it as the loss value.

def custom_loss(y_true, y_pred, uncertainty):
    squared_error = tf.square(y_true - y_pred)
    weighted_square_error = tf.cast(squared_error, dtype=tf.float64) * tf.exp(-0.2 * uncertainty)
    mean_weighted_squared_error = tf.reduce_mean(weighted_square_error)
    return mean_weighted_squared_error
In [60]:
# Create Image Data Generator and use flow_from_dataframe 
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator


train_datagen = ImageDataGenerator(rescale=1./255)
train_data = train_datagen.flow_from_dataframe(df_train,
                                                x_col='file_name',
                                                y_col='sqrt(qplume)',
                                                target_size=(217, 217),
                                                batch_size=32,  
                                                class_mode="raw", 
                                                seed=42)
val_datagen = ImageDataGenerator(rescale=1./255)
val_data = val_datagen.flow_from_dataframe(df_val,
                                                x_col='file_name',
                                                y_col='sqrt(qplume)',
                                                target_size=(217, 217), 
                                                batch_size=32,  
                                                class_mode="raw", 
                                                seed=42)

aug_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.1,
    height_shift_range=0.1,
    zoom_range=0.2,
    horizontal_flip=True,
    vertical_flip=True,
    fill_mode='nearest'
)
aug_data = aug_datagen.flow_from_dataframe(df_train,
                                                x_col='file_name',
                                                y_col='sqrt(qplume)',
                                                target_size=(217, 217), 
                                                batch_size=32,  
                                                class_mode="raw", 
                                                seed=42)

Found 6148 validated image filenames.
Found 1537 validated image filenames.
Found 6148 validated image filenames.
In [3]:
# Use convolutional neural network for regression of images
import tensorflow as tf
from tensorflow.keras.metrics import RootMeanSquaredError

tf.random.set_seed(43)

model_1 = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation="relu", input_shape=(217, 217, 3)),
    tf.keras.layers.MaxPool2D(pool_size=2),
    tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation="relu"),
    tf.keras.layers.MaxPool2D(pool_size=2),
    tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu"),
    tf.keras.layers.MaxPool2D(pool_size=2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation="relu"),
    tf.keras.layers.Dense(1)
])

# Compile the model
model_1.compile(loss=lambda y_true, y_pred: custom_loss(y_true, y_pred, uncertainty),
              optimizer=tf.keras.optimizers.Adam(),
              metrics=[RootMeanSquaredError()])
In [5]:
model_1. summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 215, 215, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 107, 107, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 105, 105, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 52, 52, 64)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 50, 50, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 80000)             0         
                                                                 
 dense (Dense)               (None, 64)                5120064   
                                                                 
 dense_1 (Dense)             (None, 1)                 65        
                                                                 
=================================================================
Total params: 5,213,377
Trainable params: 5,213,377
Non-trainable params: 0
_________________________________________________________________
In [159]:
pd.DataFrame(history_1.history).plot()
plt.title("model_1 RMSE and loss chart")
plt.show()
In [139]:
# Create a function to import an image randomly from a directory and resize it to be able to be used with our model
import random
import tensorflow as tf
import imageio
import numpy as np

def predict_and_show(model, dir1):
    directory_1 = "/".join([dir1])
    files = tf.io.gfile.glob(directory_1 + "/*.png")
    random_file = random.choice(files)
    img = imageio.imread(random_file)
    img = tf.cast(img, tf.float32) / 255.
    img = tf.image.resize(img, size=[217, 217])
    img = tf.slice(img, [0, 0, 0], [-1, -1, 3]) # extract first 3 channels
    pred = np.square(model.predict(img[tf.newaxis, ...]))
    plt.imshow(img)
    print("Prediction:", pred,random_file)
    return pred
In [140]:
pred = predict_and_show(model_1, "Pollute")

1/1 [==============================] - 0s 68ms/step

C:\Users\Steve\AppData\Local\Temp\ipykernel_11584\3092079273.py:11: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
  img = imageio.imread(random_file)

Prediction: [[253.81459]] Pollute\ang20191016t172526-B_r3448_c516-plume.png
In [156]:
# Create a function that will do model.predict on all images in directory and save to pandas dataframe
def predict_and_store_df(model, dir1):
    directory_1 = "/".join([dir1])
    files = tf.io.gfile.glob(directory_1 + "/*.png")
    results = []
    for file in files:
        img = imageio.imread(file)
        img = tf.cast(img, tf.float32) / 255.
        img = tf.image.resize(img, size=[217, 217])
        img = tf.slice(img, [0, 0, 0], [-1, -1, 3]) # extract first 3 channels
        pred = np.square(model.predict(img[tf.newaxis, ...]))
        results.append({'file': file, 'prediction': pred})
    df = pd.DataFrame(results)
    return df
In [144]:
# call function
df_pred_1 = predict_and_store_df(model_1, "Pollute")

1/1 [==============================] - 0s 21ms/step
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C:\Users\Steve\AppData\Local\Temp\ipykernel_11584\4089737092.py:7: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
  img = imageio.imread(file)

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In [145]:
# rename and clean
df_pred_1.rename(columns={'file':'file_name'}, inplace=True)
df_pred_1['file_name'] = df_pred_1['file_name'].str[8:]
df_pred_1['prediction'] = df_pred_1['prediction'].astype(str)
df_pred_1['prediction'] = df_pred_1['prediction'].str.replace("[", "").str.replace("]", "").astype(float)

C:\Users\Steve\AppData\Local\Temp\ipykernel_11584\2333466532.py:5: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  df_pred_1['prediction'] = df_pred_1['prediction'].str.replace("[", "").str.replace("]", "").astype(float)
In [146]:
df_pred_1
Out[146]:
file_name prediction
0 ang20190922t192642-2_r4578_c217-plume.png 575.39430
1 ang20190922t192642-4_r4928_c373-plume.png 241.89496
2 ang20190922t192642-5_r6423_c113-plume.png 535.24176
3 ang20190922t192642-A_r3270_c384-plume.png 193.10875
4 ang20190922t192642-B_r4034_c360-plume.png 327.63420
... ... ...
6817 GAO20220506t195745p0000-A_r827_c3724-plume.png 1045.67160
6818 GAO20220506t195745p0000-B_r730_c3470-plume.png 907.68170
6819 GAO20220506t200303p0000-A_r465_c1807-plume.png 810.57270
6820 GAO20220506t200303p0000-B_r344_c1549-plume.png 1083.88170
6821 GAO20220506t200303p0000-C_r345_c1650-plume.png 1118.11540

6822 rows × 2 columns

In [ ]:
# clean
df7['file_name'] = df7['file_name'].str[8:]
In [147]:
df7
Out[147]:
level_0 index source_id candidate_id qplume sigma_qplume file_name log(qplume) log(sigma_qplume) sqrt(qplume)
0 1 1.0 P00002 ang20190922t192642-4 420.725207 147.155782 ang20190922t192642-4_r4928_c373-plume.png 6.041980 4.991492 20.511587
1 2 2.0 P00003 ang20190922t192642-5 1051.909600 339.267587 ang20190922t192642-5_r6423_c113-plume.png 6.958362 5.826789 32.433156
2 4 4.0 P00005 ang20190922t194340-2 870.605019 435.968147 ang20190922t194340-2_r2642_c324-plume.png 6.769188 6.077569 29.506017
3 6 6.0 P00007 ang20190923t170747-12 753.524542 149.873151 ang20190923t170747-12_r8630_c560-plume.png 6.624762 5.009789 27.450401
4 12 12.0 P00013 ang20190923t174142-14 375.447796 130.455319 ang20190923t174142-14_r7604_c458-plume.png 5.928119 4.871031 19.376475
... ... ... ... ... ... ... ... ... ... ...
6930 7674 8741.0 R0286 GAO20220506t183449p0000-A 1164.747653 373.678837 GAO20220506t183449p0000-A_r2647_c2843-plume.png 7.060260 5.923397 34.128400
6931 7675 8742.0 R0287 GAO20220506t194111p0000-A 1021.946826 388.520049 GAO20220506t194111p0000-A_r639_c3273-plume.png 6.929465 5.962345 31.967903
6932 7682 8749.0 R0292 ang20220514t182953-A 72.882097 24.608781 ang20220514t182953-A_r1288_c300-plume.png 4.288843 3.203103 8.537101
6933 7683 8750.0 R0292 ang20220514t183955-A 240.217310 89.888867 ang20220514t183955-A_r1294_c278-plume.png 5.481544 4.498574 15.498945
6934 7684 8751.0 R0293 ang20220514t190216-A 488.787135 88.267676 ang20220514t190216-A_r1729_c286-plume.png 6.191927 4.480374 22.108531

6935 rows × 10 columns

In [148]:
# Make new df by joining on "file_name" column
compare_df_1 = pd.merge(df_pred_1, df7, on='file_name', how='inner')
In [149]:
compare_df_1
Out[149]:
file_name prediction level_0 index source_id candidate_id qplume sigma_qplume log(qplume) log(sigma_qplume) sqrt(qplume)
0 ang20190922t192642-4_r4928_c373-plume.png 241.89496 1 1.0 P00002 ang20190922t192642-4 420.725207 147.155782 6.041980 4.991492 20.511587
1 ang20190922t192642-5_r6423_c113-plume.png 535.24176 2 2.0 P00003 ang20190922t192642-5 1051.909600 339.267587 6.958362 5.826789 32.433156
2 ang20190922t192642-A_r3270_c384-plume.png 193.10875 759 759.0 P00156 ang20190922t192642-A 296.676732 98.755811 5.692643 4.592650 17.224306
3 ang20190922t192642-A_r3270_c384-plume.png 193.10875 5758 4234.0 P00156 ang20190922t192642-A 296.676732 98.755810 5.692643 4.592650 17.224306
4 ang20190922t192642-B_r4034_c360-plume.png 327.63420 760 760.0 P00119 ang20190922t192642-B 640.968143 190.138173 6.462980 5.247751 25.317349
... ... ... ... ... ... ... ... ... ... ... ...
6930 GAO20220506t183449p0000-B_r4623_c5161-plume.png 420.47086 7673 8740.0 R0285 GAO20220506t183449p0000-B 375.228433 73.280045 5.927535 4.294288 19.370814
6931 GAO20220506t193353p0000-B_r2080_c1049-plume.png 1010.54300 7300 8195.0 R0286 GAO20220506t193353p0000-B 902.403658 227.633798 6.805062 5.427738 30.040034
6932 GAO20220506t193353p0000-B_r2080_c1049-plume.png 1010.54300 7668 8735.0 R0281 GAO20220506t193353p0000-B 902.403658 227.633798 6.805062 5.427738 30.040034
6933 GAO20220506t194111p0000-A_r639_c3273-plume.png 656.09106 7306 8201.0 R0291 GAO20220506t194111p0000-A 1021.946826 388.520049 6.929465 5.962345 31.967903
6934 GAO20220506t194111p0000-A_r639_c3273-plume.png 656.09106 7675 8742.0 R0287 GAO20220506t194111p0000-A 1021.946826 388.520049 6.929465 5.962345 31.967903

6935 rows × 11 columns

In [150]:
# Scatter plot of ground truth
compare_df_1 = compare_df_1.drop('index', axis=1)
compare_df_1 = compare_df_1.reset_index()
compare_df_1.plot(kind='scatter', x='index', y='qplume')
plt.title('Scatter plot of qplume - Ground Truth')
plt.show()
In [151]:
# Scatter plot of Model 1
compare_df_1.plot(kind='scatter', x='index', y='prediction')
plt.title('Scatter plot of qplume - Model 1 Prediction')
plt.show()
In [152]:
RMSE_1 = np.sqrt(np.mean((compare_df_1['qplume'] - compare_df_1['prediction'])**2))
print("Model_1","RMSE: ", RMSE_1)

Model_1 RMSE:  190.5015420424947
In [148]:
# save results to csv file
compare_df_1.to_csv("model_1_preds.csv", index=False)
In [221]:
# Save tensorflow models
model_1.save('Regression_model_sqrt.h5')
In [33]:
# Create a function that will do model.predict on all images in directory and save to pandas dataframe
import imageio
def predict_and_store_df(model, dir1):
    directory_1 = "/".join([dir1])
    files = tf.io.gfile.glob(directory_1 + "/*.png")
    results = []
    for file in files:
        img = imageio.imread(file)
        img = tf.cast(img, tf.float32) / 255.
        img = tf.image.resize(img, size=[217, 217])
        img = tf.slice(img, [0, 0, 0], [-1, -1, 3]) # extract first 3 channels
        pred = np.square(model.predict(img[tf.newaxis, ...]))
        results.append({'file': file, 'prediction': pred})
    df = pd.DataFrame(results)
    df['prediction'] = df['prediction'].astype(str)
    df['prediction'] = df['prediction'].str.replace("[", "").str.replace("]", "").astype(float)
    return df
In [35]:
df_1 = predict_and_store_df(model_1, "test")
df_1.to_csv("submission.csv",index=False)
df_1

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C:\Users\Steve\AppData\Local\Temp\ipykernel_14356\3402347014.py:8: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
  img = imageio.imread(file)

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C:\Users\Steve\AppData\Local\Temp\ipykernel_14356\3402347014.py:16: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  df['prediction'] = df['prediction'].str.replace("[", "").str.replace("]", "").astype(float)
Out[35]:
file prediction
0 test\01A.png 604.15370
1 test\02A.png 370.29840
2 test\03A.png 217.86560
3 test\04A.png 247.24920
4 test\05A.png 355.13205
5 test\06A.png 302.87183
6 test\07A.png 259.27304
7 test\08A.png 252.94960
8 test\09A.png 713.42896
9 test\10A.png 248.57156
10 test\11A.png 754.31230
11 test\12A.png 363.86475
12 test\13A.png 237.59589
13 test\14A.png 237.80020
14 test\15A.png 397.95703
15 test\16A.png 294.72174
16 test\17A.png 270.01562
17 test\18A.png 510.06635
18 test\19A.png 229.96875
19 test\20A.png 231.33342