Modeling Quasar time series with Neural processes in Python
Project description
QNPy Documentation
Introduction
In exploring the diverse features of quasar light curves, a significant challenge arises from recurring gaps in observations, which pose a primary limitation. This obstacle, compounded by the inherent irregularities in data collection cadences, presents a formidable barrier. This complexity will be particularly pronounced when dealing with data that is going to come from the Legacy Survey of Space and Time (LSST), featuring seasonal gaps. Existing strategies, while effective, entail substantial computational costs. To address the complex nature of quasar light curve modeling, our package QNPy has been developed to efficiently model quasar light curves using Conditional Neural Processes.
Conditional Neural Processes
Conditional Neural Processes (CNPs) are a type of neural network architecture designed for flexible and probabilistic function learning. They are particularly well-suited for tasks involving conditional predictions and have applications in areas like regression, classification, and generative modeling.
The core idea behind CNPs is to learn a distribution over functions conditioned on input-output pairs. They are capable of making predictions not only for specific inputs seen during training but also for new inputs that were not present in the training data.
The CNP is a model designed for analyzing continuous-time light curves, characterized by time instances (x) and corresponding fluxes (or magnitudes) (y). In the CNP framework, we consider a scenario where we have target inputs representing time instances with unknown magnitudes. In the training process, we leverage a set of context points derived from observations, consisting of time instances (x) and observed magnitudes (y). Each pair in the context set is locally encoded using a multilayer perceptron (MLP). The resulting local encodings (Rc) are then aggregated through mean pooling to form a global representation (R). The global representation (R) is a condensed feature representation of the entire context set. This representation, along with the target input (xt), is fed into a decoder MLP. The decoder produces the mean and variance of the predictive distribution for the target output.
Key features of Conditional Neural Processes include:
-
Conditional Predictions: CNPs can provide predictions for a target output given a set of input-output pairs, allowing for context-aware predictions.
-
Flexibility: CNPs are versatile and can adapt to various types of data and tasks. They are not limited to a specific functional form, making them suitable for a wide range of applications.
-
Probabilistic Outputs: CNPs provide uncertainty estimates in the form of predictive distributions. This makes them valuable in situations where uncertainty quantification is crucial.
-
Scalability: CNPs can handle different input and output dimensions, making them scalable to various data types and problem complexities.
In summary, Conditional Neural Processes are a powerful framework for conditional function learning that offers flexibility, probabilistic predictions, and scalability across different tasks. They have shown effectiveness in tasks such as few-shot learning, meta-learning, and regression with uncertainty estimation, making them a great tool for modeling the light curves of quasars.
Self Organizing Maps
Conditional Neural Processes excel at learning complex patterns in data with recurring gaps. However, application to larger datasets requires novel methods to prioritize efficiency and effectively capture subtle trends in the data. Self Organizing Maps (SOMs) provide both these advantages. SOMs provide an unsupervised clustering algorithm that can be trained quickly and include new data points without the need to train over every data point again. Thus, we present QNPy as an ensemble model of SOMs and CNPs.
SOMs comprise a network of nodes mapped onto a (usually) two-dimensional grid. Each node has an input weight associated with it. As the SOM trains on the input data, each input point is assigned a Best Matching Unit (BMU) where the node is at the minimum Euclidean distance from the input. Then, the BMU is updated to match the input data point (the amount that the node moves is dependent on the learning rate). Furthermore, each node can affect neighboring nodes via a neighborhood function (usually Gaussian).
Once the training is ended, each input data point is assigned to a cluster depending on the final BMU. Thus at the end, each node provides a cluster. These can be the final cluster or the distance matrix (a matrix containing the distance of each node with each of the other nodes) of the SOM can be used to group different nodes into more hierarchical clusters. This is done by calculating gradients between the nodes until the lowest node is reached. (For more info, refer to Hamel and Brown).
In QNPy, we treat each light curve as a data point and the magnitudes are the features. Thus, the SOM can effectively parse topological differences in the light curves. These differences allow the CNP to train on similar light curves and effectively capture subtle differences in the modeled light curves. In addition, the clusters now allow for CNPs to be trained in parallel on smaller batches of data, which allows for a massive speed-up in the training time of the QNPy package.
The SOM is based on the minisom package which uses NumPy packages to handle the input data. Thus, every input data point must have the same length. We handle this similarly with the CNP by padding all the light curves to the same length. We also scale the light curves to treat different magnitude ranges differently.
Thus, SOMs provide a useful companion to the CNPs to form an ensemble model with improved speed and accuracy in modeling large amounts of light curve data.
Installation
To install QNPy, use the following command:
.. code-block:: bash
pip install QNPy
Requirements
This package contains a requirements.txt file with all the dependencies that need to be satisfied before you can use it as a standalone package. To install all requirements at once, navigate to the directory where you downloaded your package (where the requirements.txt file is) and run:
.. code-block:: bash
pip install -r requirements.txt
You are now ready to use the QNPy package.
Special note: If you have python >3.9 on your local machine you will encounter some requirements conflicts with torch and numpy versions. In this case, we recomend creating a virtual enviroment using conda:
.. code-block:: bash
conda create -n myenv python=3.9
then you have to activate the virtuel enviroment:
.. code-block:: bash
conda activate "The name of your virtuel enviroment"
After virtual enviroment is activated you can install QNPy and the requirements.txt file in your newly created enviroment.
.. code-block:: virtual enviroment
pip install QNPy
.. code-block:: virtual enviroment
pip install -r requirements.txt
Examples
Check out the Tutorial folder here for notebooks that guide you through the process of using this package. There will be teo tutorial folders. The "QNPy without clustering: Tutorial" folder includes examples for using each of the modules separately. Additionally, you'll find an example of how your light curves should look in the Light_curves folder. The "QNPy with clustering: Tutorial" folder includes examples for using the Clustering_with_SOM module for single band and multiband clustering. You will also find example of how your light curves should look like in the folders Light_curves and Light_curves_Multiband.
Folder Structure
The QNPy automatically creates folders for saving plots, data and saves your trained SOM and CNP. The only requirement for the file structure in SOM module is to save light curves before the module and choose directories to save plots and models during the module's runtime. The files can be saved under any folder as desired and the file name can be given as an input into the loading function.
In the case of multi-band light curves only, the light curves should be saved under a directory (can be named anything) with the filters saved as subfolders. Then, each light curve should be saved as a CSV file with the id as the file name. For example, if you have a light curve in the g filter with ID 10422 and you want to save it in a folder known as Light_Curves, it should be saved under the directory Light_Curves/g/10422.csv. This is the standard recommendation for multi-band data. Then, once the clusters are created, it is easy to either point QNPy to one of the filters of a cluster or to manually flatten the file and provide all the light curves to QNPy. However, QNPy does not yet support explicit multi-band clustering.
For all QNPy modules, your data must contain: mjd - MJD or time, mag - magnitude, and magerr - magnitude error.
Before running the script, you can manually create the following folders in the directory where your Python notebook is located:
./preproc/- for saving the transformed data./padded_lc/- for saving the padded light curves./dataset/train/,./dataset/test/,./dataset/val/- for organizing your dataset./output/prediction/test/data/,./output/prediction/test/plots/- for organizing prediction results./output/prediction/train/data/,./output/prediction/train/plots/- for organizing training results./output/prediction/val/data/,./output/prediction/val/plots/- for organizing validation results
Modules and Their Functions
Clustering with SOM
Clustering_with_SOM.py
In the clustering module, we first load the light curves from the directory. This also creates the ids from the file names. Thus, it is recommended to have the same light curves saved across all the different bands. Then, we pad the light curves to make them all the same length. In QNPy, we have seen that we require at least 100 data points for accurate modeling. Thus, we recommend that the light curves be padded to at least 100 points (even if the longest curve is under 100 points, which can be controlled through a keyword in the padding function). Finally, we scale the light curves. We have provided many different scalers including minmax, standard and robust scalers. Our default scaler is an adapted version of a minmax scaler that scales all the data to the range [-2,2].
Then, a SOM is trained on the scaled data. The SOM contains different tunable hyperparameters to better adapt to different data sets. These hyperparameters can be tested with different metrics including quantization error, topographical error, silhouette score, davies-bouldin index, or calinski-harabasz score. The trained SOM can be saved as well.
The trained SOM is then used to assign the IDs to different clusters. Then they can be saved into different folders.
We also provide different plots for visualization of the data. These will be described in the plotting functions.
Described below are the functions used in the module:
.. code-block:: python
def Plot_Lc(Light_Curve,header = 'Light Curve',save_fig = False,filename = 'Figure',x_axis = 'mjd',return_fig = False):
'''
Plots light curves interactively. Adapted from https://github.com/DamirBogdan39/time-series-analysis/tree/main
Parameters
----------
Light_Curve: Dataframe
The light curve to plot. Should be in a dataframe with mjd (or any x axis), mag and magerr
header: str
The header of the file
save_fig: bool
Whether to save the figure
filename: str
What to name the saved html file
x_axis: str
What to label the x axis
return_fig: bool
Whether the figure is returned
Returns
--------
Figure:
The interactive plot of the light curve
'''
.. code-block:: python
def Load_Light_Curves(folder,one_filter = True,filters = 'a',id_list = None):
'''
Loads light curves from a specified folder. Can be used to load either multiple filters or just one filter
Parameters
----------
folder: str
The folder where the light curves are stored
one_filter: bool
If set to true, the light curves are only stored in one folder without filters
filters: list or str(if each filter is a single letter)
The filters that are to be loaded. Each filter should have a subfolder named after it if there are more than one filters
id_list: list of str or None
The subset of IDs to load. If None, retrieves all files in the given folder. NOTE: make sure the ids are strings
Returns
--------
light_curves: list of lists of dataframes
The list of light curves arranged by filter
ids: list
The ids of the light curves (Ensure that they are the same in all filters)
'''
.. code-block:: python
def Pad_Light_Curves(light_curves,minimum_length = 100,save_padded_lcs = False,padded_lcs_save_path = './',ids = None):
'''
Pads the light curves with the mean value at the end of the curve
Parameters
----------
light_curves: lists of dataframes
The light curves stored in a list
minimum_length: int
The minimum length to pad to
save_padded_lcs: bool
If True, will save the light curves into a folder known as Padded_Lc in the specified directory
padded_lcs_save_path: str
The directory to save the light curves in
ids: list of str
A list of the ids. Must provided in order to save the light curves
Returns
--------
light_curves: list of lists
The new padded light curves
'''
.. code-block:: python
def scale_curves(light_curves,what_scaler = 'default',scale_times = True):
'''
Scaling the curves (from a single filter) from the choice of minmax, standard and robust. By default, it scales to a range of [-2,2]
Parameters
----------
light_curves: list of dataframes
The light curves stored in a list.
what_scaler: string
The type of scaler to use. There are default (see above), standard scaler, min-max scaler and robust scalers available
scale_times: bool
Whether to scale the time axis as well (These are always scaled to the default scaler)
Returns
--------
scaled_curves: np.ndarray
The scaled light curves
scaled_times:np.ndarray
The scaled time steps. It is an empty list if the keyword scale_times is False
'''
.. code-block:: python
def SOM_1D(scaled_curves,som_x = None,som_y = None,learning_rate = 0.1,sigma = 1.0,topology = 'rectangular',pca_init = True,\
neighborhood_function='gaussian',train_mode = 'random',batch_size = 5,epochs = 50000,save_som = True,\
model_save_path = './',random_seed = 21,stat = 'q',plot_frequency = 100,early_stopping_no = None):
'''
Training a SOM on ONE dimensional data (The magnitude of the light curves)
Parameters
----------
scaled_curves: list of dataframes
The scaled light curves stored in a list.
som_x: int
The x size of the SOM. If None is given, make sure the som_y is None as well. Then, it chooses the recommended SOM
size of sqrt(sqrt(length))
som_y: int
The y size of the SOM. If None is given, make sure the som_x is None as well. Then, it chooses the recommended SOM
size of sqrt(sqrt(length))
learning_rate: float
How much the SOM learns from the new data that it sees
sigma: float
The effect each node has on its neighboring nodes
topology: 'rectangular' or 'hexagonal':
The topology of the SOM. Note that visualizations are mainly built for the rectangular at the moment.
pca_init: bool
Whether to initialize the SOM weights randomly or to initialize by PCA of the input data
neighborhood_function: str
Choose from 'gaussian','mexican hat','bubble', or 'triangle'. These affect the influence of a node on its neighbors
train_mode:'random' or 'all'
When chosen random, it chooses a random curve each epoch. When trained on all, it batches the data and trains on every
light curve for a certain number of epochs.
batch_size: int
How big the batch is for the 'all' train mode. The smaller the batch size, the finer the progress bar displayed
epochs: int
This is defined in two ways. If the train_mode is random, then it is the number of iterations that the SOM runs on.
If it is all, then it is the number of times that the SOM trains on each input datapoint. Note that the lr and sigma
decay in each epoch.
save_som: bool
Whether to save the trained SOM
model_save_path:str
The file to save the SOM in
random_seed:int
The starting state of the random weights of the SOM. Use for reproducibility
stat: 'q','t', or 'qt'
Whether to record the quantization error, topographical error or both. Note that calculating them is expensive
plot_frequency: int
The number of epochs
early_stopping_no: int or None
The number of batches to process before stopping. Use None if you should train on all
Returns
--------
som_model:
The trained SOM that can be saved or used for analysis
q_error: list
The quantization errors recorded
t_error: list
The topographic errors recorded
indices_to_plot: list
The indices to plot for the quantization or/and topographic errors
'''
.. code-block:: python
def plot_training(training_metric_results,metric,plotting_frequency,indices_to_plot,figsize = (10,10),save_figs = True,fig_save_path = './'):
'''
Plots the metric given (quantization error or topographic error)
Parameters
----------
training_metric_results: list
The result obtained from the SOM training
metric: str
Name of the metric
plotting_frequency: int
How much was the plotting frequency set during the SOM training
indices_to_plot: list
The indices to plot obtained from the SOM training
figsize: tuple
The size of the figure
save_figs: bool
Whether to save the figure or not
fig_save_path:str
Where to save the figure. Note that it creates a directory called Plots in the location given.
Returns
--------
Plot:
The plot of the metric
'''
.. code-block:: python
def Plot_SOM_Scaled_Average(som_model,scaled_curves,dba = True,figsize = (10,10),save_figs = True,figs_save_path = './',\
plot_weights = True,plot_avg = True,plot_background = True,one_fig = True,show_fig = True):
'''
Plotting the SOM Clusters with the average light curve and the SOM weights of each cluster. The average can be either simple mean
or using a dba averaging method (https://github.com/fpetitjean/DBA)
Parameters
----------
som_model:
The trained SOM
scaled_curves: np.ndarray
The scaled curves that were the input for training
dba: bool
Whether to use Dynamic Barymetric Time Averaging
figsize: tuple
The size of the figure
save_figs: bool
Whether to save the figure or not
fig_save_path: str
Where to save the figure. Note that it creates a directory called Plots in the location given.
plot_avg: bool
Whether to plot the mean light curve of the cluster
plot_weights: bool
Whether to plot the SOM weight of the cluster
plot_background: bool
Whether to plot the light curves that make up the cluster
one_fig: bool
Whether to plot all the clusters into one figure or seperate figures
show_fig: bool
Whether to show each of the plots in the seperate figures case
Returns
--------
Plot:
The plots of the clusters
'''
.. code-block:: python
def SOM_Distance_Map(som_model,figsize = (5,5),cmap = 'YlOrRd',save_figs = False,figs_save_path = './'):
'''
Plots a heatmap of the SOM Nodes. The brighter, the further away they are from their neighbors
Parameters
----------
som_model:
The trained SOM
cmap: str
The matplotlib based color scale to use for the plots
figsize: tuple
The size of the figure
save_figs: bool
Whether to save the figure or not
fig_save_path: str
Where to save the figure. Note that it creates a directory called Plots in the location given.
Returns
--------
Plot:
The heatmap plot
'''
.. code-block:: python
def SOM_Activation_Map(som_model,figsize = (5,5),cmap = 'YlOrRd',save_figs = False,figs_save_path = './'):
'''
Plots a heatmap of the SOM Nodes. The brighter, the more light curves activate the SOM
Parameters
----------
som_model:
The trained SOM
cmap: str
The matplotlib based color scale to use for the plots
figsize: tuple
The size of the figure
save_figs: bool
Whether to save the figure or not
fig_save_path: str
Where to save the figure. Note that it creates a directory called Plots in the location given.
Returns
--------
Plot:
The heatmap plot
'''
.. code-block:: python
def Assign_Cluster_Labels(som_model,scaled_curves,ids):
'''
Assigns Cluster labels to each of the curves, making a dataframe with their ids
Parameters
----------
som_model:
The trained SOM
scaled_curves: np.ndarray
The scaled curves that were used to train the SOM
ids: list
The ids of the curves
Returns
--------
cluster_df: Dataframe
A map matching each of the cluster ids with the cluster they belong to
'''
.. code-block:: python
def SOM_Clusters_Histogram(cluster_map,color = 'tab:blue',save_figs = True,figs_save_path = './',figsize = (5,5)):
'''
Plots a heatmap of the SOM Nodes. The brighter, the further away they are from their neighbors
Parameters
----------
cluster_map:
The dataframe with each id and the cluster that it belongs to
color: str
The color to plot the histogram
save_figs: bool
Whether to save the figure or not
fig_save_path: str
Where to save the figure. Note that it creates a directory called Plots in the location given.
figsize: tuple
The size of the figure
Returns
--------
Plot:
The Histogram of how many curves are in each cluster
'''
.. code-block:: python
def findMin(x, y, umat):
'''
Finds the minimum node in the unified matrix when given the x and y coordinate
Parameters
----------
x: int
The x position of the given input node
y: int
The y position of the given input node
umat: np.ndarry
The unified distance matrix of the nodes of the SOM
Returns
--------
minx, miny:
The minumum x node and minimum y node
'''
.. code-block:: python
def findInternalNode(x, y, umat):
'''
Finds the minimum node in the unified matrix when given the x and y coordinate, taking into account if the current node is min
Parameters
----------
x: int
The x position of the given input node
y: int
The y position of the given input node
umat: np.ndarry
The unified distance matrix of the nodes of the SOM
Returns
--------
minx, miny:
The minumum x node and minimum y node
'''
.. code-block:: python
def Get_Gradient_Cluster(som):
'''
Finds the center of the gradient for each node of the SOM
Parameters
----------
som: int
The trained SOM
Returns
--------
cluster_centers:
The center nodes that become the new gradient clusters
cluster_pos:
The original SOM cluster centers
'''
.. code-block:: python
def Normal_Cluster_to_Grad(cluster_map,gradient_cluster_map):
'''
Maps the normal cluster map to the gradient clusters
Parameters
----------
cluster_map: pd.DataFrame
The map of the ids to the original SOM node clusters
gradient_cluster_map: pd.DataFrame
The map of the ids to the gradient SOM node clusters
Returns
--------
joint_map:
Mapping of each SOM node cluster to the gradient cluster
'''
.. code-block:: python
def Gradient_Cluster_Map(som,scaled_curves,ids,dimension = '1D',fill = 'mean',interpolation_kind = 'cubic',clusters = None,som_x = None,som_y = None):
'''
Translates the SOM nodes into larger clusters based on their gradients. Implementation of
https://homepage.cs.uri.edu/faculty/hamel/pubs/improved-umat-dmin11.pdf
Parameters
----------
som_model:
The trained SOM
scaled_curves: np.ndarray
The scaled curves used to train the SOM
ids: list
The ids of the curves
dimension: str
If 1D, does 1D clustering, else multivariate
fill: str
'mean' or 'interpolate'. Either the empty values are filled with the mean or they are interpolated with a function
interpolation_kind:
Any of the scipy.interp1d interpolation kinds. Recommended to use cubic
clusters:
The clusters that the ids are in (only for multi-variate)
som_x: int
The x-dimensions of the SOM
som_y: int
The y-dimensions of the SOM
Returns
--------
cluster_map:
The new clusters that the ids are in
'''
.. code-block:: python
def matplotlib_cmap_to_plotly(cmap, entries):
'''
Creates a colorscale used to create an interactive plot
Parameters
----------
cmap:
The colormap
entries:
The colormap entries
Returns
--------
colorscale:
The colorscale for the interactive plot
'''
.. code-block:: python
def plotStarburstMap(som):
'''
Interactive plot of the distance map and gradients of the SOM
Parameters
----------
som:
The trained SOM
Returns
--------
Plot of the distance map and gradients
'''
.. code-block:: python
def outliers_detection(clusters_df,som,scaled_curves,ids,outlier_percentage = 0.2):
'''
Gives the percentage of the clusters that have high quanitization errors (defined by percentile) for each cluster
Parameters
----------
clusters_df:
A map of each of the ids to the clusters
som:
The trained SOM
scaled_curves: np.ndarray
The scaled curves used to train the SOM
ids: list
The ids of the curves
outlier_percentage: float
This top percentile that defines an outlier
Returns
--------
Plots:
Distribution of Outliers per cluster and distribution of quantization error
'''
.. code-block:: python
def Cluster_Metrics(scaled_curves,cluster_map,metric = 'Silhoutte'):
'''
Measures metrics related to the clustering
Parameters
----------
scaled_curves: np.ndarray
The scaled curves used to train the SOM
cluster_map: pd.Dataframe
A map of each of the ids to the clusters
metric: str
The metric to be measured. It can be Silhoutte, DBI or CH. This is for silhoutte score, Davies-Bouldin index and calinski-harabasz score
Returns
--------
score:
The metric that is calculated
'''
.. code-block:: python
def save_chosen_cluster(chosen_cluster,cluster_map,one_filter = True,filters = 'a',overwrite = True,save_path = './',source_path = './Light_Curves'):
'''
Saves the chosen cluster into a folder
Parameters
----------
chosen_cluster: int
The cluster to save
cluster_map: pd.Dataframe
A map of each of the ids to the clusters
one_filter: bool
Whether to save just one filter or all the filters
filters: str
The filters to save
overwrite: bool
Whether to overwrite the current folder
save_path: str
The path to save to. This creates a folder for the cluster in that directory
source_path: str
The path that the light curves are saved in. If multifilter, provide the entire larger folder.
'''
.. code-block:: python
def scale_to_range(series, min_val=-2, max_val=2):
'''
Scales a series to a range
Parameters
----------
series: pd.Series
The series to scale
min_val: int
The minimum value to scale to
max_val: int
The maximum value to scale to
Returns
--------
scaled_series:
The scaled series between the max and min values
'''
.. code-block:: python
def masked_euclidean_distance(data1, data2, mask):
'''
Calculates the masked euclidean distance between two arrays using a common mask
Parameters
----------
data1: np.ndarray
The first array
data2: np.ndarray
The second array
mask: np.ma.mask
The mask used to get the distance
Returns
--------
masked_distance:
The masked distance measure
'''
.. code-block: python
def multi_band_processing(light_curves,ids,filter_names = 'ugriz',return_wide = False):
'''
Processes the light curves into a wide table
Parameters
----------
light_curves:
The light curves to be used
ids: list,array
The ids of the quasars
filter_names: list or str(if the filters are one letter)
The filters that are used
return_wide: bool
Whether to return the wide table or a flat table
Returns
--------
light_curves_wide:
The pivot table of the light curves with time steps
light_curves_wide.isna():
The mask used with the wide light curves
OR
light_curves_flat:
The flattened pivot table of the light curves
mask_flat:
The mask used
'''
.. code-block:: python
def multi_band_clustering(light_curves,ids,filter_names = 'ugriz',som_x = None,som_y = None,sigma = 1.0,learning_rate = 0.5,\
num_iterations = 2,batch_size = 5,early_stopping_no = None):
'''
Multiband light curve clustering
Parameters
----------
light_curves:
The light curves to be used
ids: list,array
The ids of the quasars
filter_names: list or str(if the filters are one letter)
The filters that are used
som_x: int
The x size of the SOM. If None is given, make sure the som_y is None as well. Then, it chooses the recommended SOM
size of sqrt(sqrt(length))
som_y: int
The y size of the SOM. If None is given, make sure the som_x is None as well. Then, it chooses the recommended SOM
size of sqrt(sqrt(length))
sigma: float
The effect each node has on its neighboring nodes
learning_rate: float
How much the SOM learns from the new data that it sees
num_iterations: int
The number of iterations that the som is trained on each batch
batch_size: int
The size of each batch
early_stopping_no: int or None
The number of batches to process before stopping. Use None if you should train on all
Returns
--------
som:
The trained SOM
processed_light_curve:
The flat light curves used for the SOM
processed_mask:
The mask used for the SOM
'''
.. code-block:: python
def find_cluster_and_quantization_errors(som,data,masks):
'''
Finding the clusters and the quantization errors from the trained 2D SOM
Parameters
----------
som:
The trained SOM
data:
The processed light curves from the trained SOM
masks:
The masks used from the trained SOM
Returns
--------
min_clusters:
The clusters for each of the data points
quantization_error:
The quantization error of each of the data points
'''
.. code-block:: python
def Get_Gradient_Cluster_2D(som,fill = 'mean',interpolation_kind = 'cubic'):
'''
Finding the gradient clusters from the 2D SOM
Parameters
----------
som:
The trained SOM
fill: str
'mean' or 'interpolate'. Either the empty values are filled with the mean or they are interpolated with a function
interpolation_kind:
Any of the scipy.interp1d interpolation kinds. Recommended to use cubic
Returns
--------
cluster_centers:
The cluster centers
cluster_pos:
The cluster positions
'''
.. code-block:: python
def create_dir_save_plot(path,plot_name):
'''
If there is no folder named plots in the path, it creates one and saves an figure
Parameters
----------
path: str
The path to create the Plots folder in
plot_name:
The name to save the plot under
'''
.. code-block:: python
def tolerant_mean(arrs):
'''
Calculates the mean of arrays without them having to be the same length
Parameters
----------
arrs:
The arrays to calculate the mean for
Returns
--------
mean:
The tolerant mean of the arrays
std:
The tolerant std of the arrays
'''
.. code-block:: python
def get_best_grid(number_of_objects):
'''
Creates a grid that is optimal for the number of objects
Parameters
----------
number_of_objects: int
The number of objects to make the grid for
Returns
--------
rows:
The number of rows for the grid
cols:
The number of cols for the grid
'''
.. code-block:: python
def Averaging_Clusters(chosen_cluster,cluster_map,lcs,plot = True,dba = True):
'''
Creating a representation of the chosen cluster with the light curves and the average light curve
Parameters
----------
chosen_cluster: int
The cluster of interest
cluster_map: pd.Dataframe
A map of each of the ids to the clusters
lcs: list of list of pd.Dataframes
The light curves (provide the input from just one filter)
plot: bool
Whether to plot or just return the average value
dba: bool
Whether to use Dynamic Barymetric Time Averaging or to use a simple mean of the light curves
Returns
--------
average_x:
The x_axis (i.e timesteps) of the average light curve
average_y:
The y_axis (i.e magnitudes) of the average light curve
x:
The timesteps of all the light curves concatenated into one array
y:
The magnitudes of all the light curves concatenated into one array
len(x):
The length of all the light curves
'''
.. code-block:: python
def Plot_All_Clusters(cluster_map,lcs,color = 'tab:blue',dba = True,figsize = (10,10),save_figs = True,figs_save_path = './'):
'''
Plots all of the clusters on a magnitude plot with the average representation included
Parameters
----------
cluster_map: pd.Dataframe
A map of each of the ids to the clusters
lcs: list of list of pd.Dataframes
The light curves (provide the input from just one filter)
color: str
The color to plot the averaged curve in
dba: bool
Whether to use Dynamic Barymetric Time Averaging or to use a simple mean of the light curves
figsize: tuple
The figure size
save_figs: bool
Whether to save the figure or not
figs_save_path: str
Where to save the figure. Note that it is saved under a directory called Plots in that directory.
'''
.. code-block:: python
def get_redshifts(redshifts_map):
'''
Gets all the redshifts from a redshifts map
Parameters
----------
redshifts_map: pd.Dataframe
The mapping of ids to redshifts
Returns
--------
redshifts:
The list of redshifts
'''
.. code-block:: python
def get_fvars(lcs):
'''
Calculates the variability function of the light curves
Parameters
----------
lcs: List of pd.Dataframes
The light curves of interest
Returns
--------
fvars:
The list of variability functions
'''
.. code-block:: python
def get_luminosities_and_masses(lcs, redshifts_map, H0 = 67.4, Om0 = 0.315):
'''
Randomly samples the luminosity and masses of the quasar black holes assuming a given Hubble Constant, Omega_0, and redshift
Parameters
----------
lcs: List of pd.Dataframes
The light curves of interest
redshifts_map: pd.DataFrame
The map from the ids to their redshifts
H0: float
The hubble constant at z=0
Om0: float
Omega matter: density of non-relativistic matter in units of the critical density at z=0.
Returns
--------
Log_lum:
The logarithm of the luminosities
Log_Mass:
The logarithm of the masses
'''
.. code-block:: python
def Cluster_Properties(cluster_map,selected_cluster,lcs,redshifts_map = None,plot = True,return_values = False,\
the_property = 'all',save_figs = True,figs_save_path = './'):
'''
Getting the selected property of a chosen cluster
Parameters
----------
cluster_map: pd.Dataframe
A map of each of the ids to the clusters
chosen_cluster: int
The cluster of interest
lcs: list of list of pd.Dataframes
The light curves (provide the input from just one filter)
redshifts_map: pd.Dataframe
The redshift associated with each source id
plot: bool
Whether to plot or just return the average value
return_values: bool
Whether to return the values for the property
the_property: str
The property to plot. Choice from z (redshift), Fvar (the variability function), Lum (luminosity), Mass, or all
save_figs: bool
Whether to save the figure or not
figs_save_path: str
Where to save the figure. Note that it is saved under a directory called Plots in that directory.
Returns
--------
return_list:
The list of the property of interest
'''
.. code-block:: python
def Cluster_Properties_Comparison(cluster_map,lcs,redshifts_map,the_property = 'Fvar',color = '#1f77b4',\
figsize = (15,15),save_figs = True,figs_save_path = './'):
'''
Plotting the property of interest for all the clusters onto one figure
Parameters
----------
cluster_map: pd.Dataframe
A map of each of the ids to the clusters
lcs: list of list of pd.Dataframes
The light curves (provide the input from just one filter)
redshifts_map: pd.Dataframe
The redshift associated with each source id
the_property: str
The property to plot. Choice from z (redshift), Fvar (the variability function), Lum (luminosity), Mass, or all
color: str
The color to make the histogram
figsize: tuple
The figure size
save_figs: bool
Whether to save the figure or not
figs_save_path: str
Where to save the figure. Note that it is saved under a directory called Plots in that directory.
Returns
--------
return_list:
The list of the property of interest
'''
.. code-block:: python
def SFplus(magnitudes):
'''
Calculates the S+ function of given light curves. S+ is the variance of magnitudes where the brightness increases
Parameters
----------
lcs: List of pd.Dataframes
The light curves of interest
Returns
--------
sfplus:
The list of S+
'''
.. code-block:: python
def SFminus(magnitudes):
'''
Calculates the S- function of given light curves. S- is the variance of magnitudes where the brightness decreases
Parameters
----------
lcs: List of pd.Dataframes
The light curves of interest
Returns
--------
sfminus:
The list of S-
'''
.. code-block:: python
def SF(magnitudes):
'''
Calculates the S function of given light curves. S is the variance of all magnitudes
Parameters
----------
lcs: List of pd.Dataframes
The light curves of interest
Returns
--------
sf:
The list of SFs
'''
.. code-block:: python
def Structure_Function(cluster_map,selected_cluster,lcs,bins,save_figs = True,figs_save_path = './'):
'''
Create the structure function for a given cluster
Parameters
----------
cluster_map: pd.Dataframe
A map of each of the ids to the clusters
selected_cluster: int
The cluster of interest
lcs: list of list of pd.Dataframes
The light curves (provide the input from just one filter)
bins:int or list
The bins to use for the structure function
save_figs: bool
Whether to save the figure or not
figs_save_path: str
Where to save the figure. Note that it is saved under a directory called Plots in that directory.
Returns
--------
S+ and S- Plot:
A plot of the S+ and S- functions for the cluster
Difference Plot:
The evolution of the normalized S+ - S- throughout the observation time of the cluster
S Plot:
The evolution of the (regular) structure function through the observation time of the cluster
'''
Preprocessing the Data
Preprocess.py
In this module, we transform data in the range [-2,2]x[-2,2] to make training faster. This module also contains two functions for cleaning the sample from outliers. If your sample does not require outlier cleaning, the functions can be skipped. Also, you can use your designed outlier cleaning function according to your data requirements. This module also contains a function for padding light curves. This function does not need to be used if your light curves have 100 or more points. It is important to emphasize that all light curves must have the same number of points in order to train the model correctly. By testing the package, we found that it is best to do backward padding with the last measured value up to 100 points. You use the padded light curves to train the model, and later, for prediction and plotting, these points are subtracted. According to the needs of the user, another method for padding can be done. The data transformation function in this module also creates subsets of the data named your_curve_name plus and your_curve_name_minus. These subsets are made with respect to errors in magnitudes and serve to augment the model's training set. The original curves are saved as the name_of_your_curve_original.
Before running this script, you must create the following folders in the directory where your Python notebook is located:
./preproc/- It is going to be the folder for saving the transformed data./padded_lc/- It is going to be the folder for saving the padded light curves./light_curves_clean/- It is going to be the folder for saving the light curves that are cleaned from outliers (this can be scipped)
Your data must contain: mjd - MJD or time, mag - magnitude, and magerr - magnitude error
.. code-block:: python
def clean_and_save_outliers(input_folder, output_folder, threshold=3.0):
"""
Clean data in CSV files within the input folder, remove outliers, and save cleaned files in the output folder.
Parameters:
input_folder (str): Path to the folder containing input CSV files.
output_folder (str): Path to the folder where cleaned CSV files will be saved.
threshold (float, optional): Threshold for outlier detection in terms of standard deviations. Default is 3.0.
"""
.. code-block:: python
def clean_save_aggregate_data(input_folder, output_folder, threshold_aggregation=5, threshold_outliers=3.0):
"""
Clean data in CSV files within the input folder, remove outliers, aggregate time and fluxes,
and save cleaned files in the output folder.
Parameters:
input_folder (str): Path to the folder containing input CSV files.
output_folder (str): Path to the folder where cleaned CSV files will be saved.
threshold_aggregation (float, optional): Threshold for time aggregation. Default is 5.
threshold_outliers (float, optional): Threshold for outlier detection in terms of standard deviations. Default is 3.0.
"""
.. code-block:: python
def backward_pad_curves(folder_path, output_folder, desired_observations=100):
"""
Backward padding the light curves with the last observed value for mag and magerr.
If your data contains 'time' values it'll add +1 for padded values,
and if your data contains 'MJD' values it will add +0.2
:param str folder_path: The path to a folder containing the .csv files.
:param str output_path: The path to a folder for saving the padded lc.
:param int desired_observations: The number of points that our package is demanding is 100 but it can be more.
:return: The padded light curves.
:rtype: object
How to use:
padding = backward_pad_curves('./light_curves', './padded_lc', desired_observations=100)
"""
.. code-block:: python
def transform(data):
"""
Transforming data into [-2,2]x[-2,2] range. This function needs to be uploaded before using it.
:param data: Your data must contain: MJD or time, mag-magnitude, and magerr-magnitude error.
:type data: object
"""
.. code-block:: python
def transform_and_save(files, DATA_SRC, DATA_DST, transform):
"""
Transforms and saves a list of CSV files. The function also saves tr coefficients as a pickle file named trcoeff.pickle.
:param list files: A list of CSV or TXT file names.
:param str DATA_SRC: The path to the folder containing the CSV or TXT files.
:param str DATA_DST: The path to the folder where the transformed CSV or TXT files will be saved.
:param function transform: The transformation function defined previously.
:return: A list of transformation coefficients for each file, where each element is a list containing the file name and the transformation coefficients Ax, Bx, Ay, and By.
:rtype: list
How to use:
number_of_points, trcoeff = transform_and_save(files, DATA_SRC, DATA_DST, transform)
"""
- SPLITTING AND TRAINING THE DATA
SPLITTING_AND_TRAINING.py
We use this module to split the data into three subsamples that will serve as a test sample, a sample for model training, and a validation sample. This module also contains functions for training and saving models. It contains the following functions that must be executed in order.
Before running this script, you must create the following folders in the directory where your Python notebook is located:
./dataset/train- folder for saving data for training after splitting your original dataset./dataset/test- folder for test data./dataset/val- folder for validation data./output- folder where you are going to save your trained model
.. code-block:: python
def create_split_folders(train_folder='./dataset/train/', test_folder='./dataset/test/', val_folder='./dataset/val/'):
"""
Creates a TRAIN, TEST, and VAL folders in the directory.
:param str train_folder: Path for saving the train data.
:param str test_folder: Path for test data.
:param str val_folder: Path for validation data.
How to use: create_split_folders(train_folder='./dataset/train/', test_folder='./dataset/test/', val_folder='./dataset/val/')
"""
.. code-block:: python
def split_data(files, DATA_SRC, TRAIN_FOLDER, TEST_FOLDER, VAL_FOLDER):
"""
Splits the data into TRAIN, TEST, and VAL folders.
:param list files: A list of CSV file names.
:param str DATA_SRC: Path to preprocessed data.
:param str TRAIN_FOLDER: Path for saving the train data.
:param str TEST_FOLDER: Path for saving the test data.
:param str VAL_FOLDER: Path for saving the validation data.
How to use: split_data(files, DATA_SRC, TRAIN_FOLDER, TEST_FOLDER, VAL_FOLDER)
"""
TRAINING
Special note for mac os users:
When creating folders with mac operating systems, hidden .DS_Store files may be created. The user must delete these files before starting training from each folder. The best way is to go into each folder individually and run the command:
.. code-block:: python
!rm -f .DS_Store
Important note: Deleting files using the delete directly in the folders does not remove hidden files.
Before running the training function you must define:
.. code-block:: python
DATA_PATH_TRAIN = "./dataset/train" - path to train folder
DATA_PATH_VAL = "./dataset/val" - path to val folder
MODEL_PATH = "./output/cnp_model.pth" - folder for saving model
BATCH_SIZE = 32 - training batch size MUST REMAIN 32
EPOCHS = 6000 - This is optional
EARLY_STOPPING_LIMIT = 3000 - This is optional
.. code-block:: python
def get_data_loader(data_path_train, data_path_val, batch_size):
"""
--- Defining train and validation loader for training process and validation
Args:
:param str data_path_train: path to train folder
:param str data_path_val: path to val folder
:param batch_size: it is recommended to be 32
How to use: trainLoader, valLoader = get_data_loader(DATA_PATH_TRAIN,BATCH SIZE)
"""
.. code-block:: python
def create_model_and_optimizer():
"""
--Defines the model as Deterministic Model, optimizer as torch optimizer, criterion as LogProbLoss, mseMetric as MSELoss and maeMetric as MAELoss
How to use: model, optimizer, criterion, mseMetric, maeMetric = create_model_and_optimizer(device)
Device has to be defined before and it can be cuda or cpu
"""
.. code-block:: python
def train_model(model, train_loader, val_loader,criterion, optimizer, num_runs, epochs, early_stopping_limit, mse_metric, maeMetric, device):
"""
-- Trains the model
Args:
model: Deterministic model
train_loader: train loader
val_loader: validation loader
criterion: criterion
optimizer: torch optimizer
num_runs: The number of trainings
epochs: epochs for training. This is optional, but minimum of 3000 is recomended
early_stopping_limit: limits the epochs for stopping the training. This is optional but minimum of 1500 is recomended
mse_metric: mse metric
mae_metric: mae metric
device: torch device cpu or cuda
How to use: If you want to save history_loss_train, history_loss_val, history_mse_train and history_mse_val for plotting you train your model like:
history_loss_train, history_loss_val, history_mse_train, history_mse_val, history_mae_train, history_mae_val, epoch_counter_train_loss, epoch_counter_train_mse, epoch_counter_train_mae, epoch_counter_val_loss, epoch_counter_val_mse, epoch_counter_val_mae = st.train_model(model, trainLoader, valLoader, criterion, optimizer, 1, 3000, 1500, mseMetric, maeMetric, device)
"""
.. code-block:: python
def save_lists_to_csv(file_names,lists):
"""
--saving the histories to lists
args:
:param list file_names: A list of file names to be used for saving the data. Each file name corresponds to a specific data list that will be saved in CSV format.
:param list lists: A list of lists containing the data to be saved. Each inner list represents a set of rows to be written to a CSV file.
How to use:
# Define the file names for saving the lists
file_names = ["history_loss_train.csv", "history_loss_val.csv", "history_mse_train.csv", "history_mse_val.csv","history_mae_train.csv", "history_mae_val.csv", "epoch_counter_train_loss.csv", "epoch_counter_train_mse.csv", "epoch_counter_train_mae.csv", "epoch_counter_val_loss.csv","epoch_counter_val_mse.csv", "epoch_counter_val_mae.csv"]
# Define the lists
lists = [history_loss_train, history_loss_val, history_mse_train, history_mse_val, history_mae_train,
history_mae_val, epoch_counter_train_loss, epoch_counter_train_mse, epoch_counter_train_mae,
epoch_counter_val_loss, epoch_counter_val_mse, epoch_counter_val_mae]
save_list= save_lists_to_csv(file_names, lists)
"""
.. code-block:: python
def plot_loss(history_loss_train_file, history_loss_val_file, epoch_counter_train_loss_file):
"""
-- plotting the history losses
Args:
returned data from test_model
How to use:
history_loss_train_file = './history_loss_train.csv' # Replace with the path to your history_loss_train CSV file
history_loss_val_file = './history_loss_val.csv' # Replace with the path to your history_loss_val CSV file
epoch_counter_train_loss_file = './epoch_counter_train_loss.csv' # Replace with the path to your epoch_counter_train_loss CSV file
logprobloss=plot_loss(history_loss_train_file, history_loss_val_file, epoch_counter_train_loss_file)
"""
.. code-block:: python
def plot_mse_metric(history_mse_train_file, history_mse_val_file, epoch_counter_train_mse_file):
"""
-- plotting the mse metric
args:
returned data from test_model
How to use:
history_mse_train_file = './history_mse_train.csv' # Replace with the path to your history_mse_train CSV file
history_mse_val_file = './history_mse_val.csv' # Replace with the path to your history_mse_val CSV file
epoch_counter_train_mse_file = './epoch_counter_train_mse.csv' # Replace with the path to your epoch_counter_train_mse CSV file
msemetric=plot_mse(history_mse_train_file, history_mse_val_file, epoch_counter_train_mse_file)
"""
.. code-block:: python
def plot_mae_metric(history_mae_train_file, history_mae_val_file, epoch_counter_train_mae_file):
"""
-- plotting the mae metric
args:
returned data from test_model
How to use:
history_mae_train_file = './history_mae_train.csv' # Replace with the path to your history_mae_train CSV file
history_mae_val_file = './history_mae_val.csv' # Replace with the path to your history_mae_val CSV file
epoch_counter_train_mae_file = './epoch_counter_train_mae.csv' # Replace with the path to your epoch_counter_train_mae CSV file
maemetric=plot_mae(history_mae_train_file, history_mae_val_file, epoch_counter_train_mae_file)
"""
.. code-block:: python
def save_model(model, MODEL_PATH):
"""
-- saving the model
Args:
model: Deterministic model
:param str MODEL_PATH: output path for saving the model
How to use: save_model(model, MODEL_PATH)
"""
- PREDICTION AND PLOTTING THE TRANSFORMED DATA, EACH CURVE INDIVIDUALLY
PREDICTION.py
We use this module for prediction and plotting of models of transformed data. Each curve will be plotted separately. It contains the following functions that must be executed in order.
Before running this script, you must create the following folders in the directory where your Python notebook is located:
./output/predictions/train/plots- folder for saving training plots./output/predictions/test/plots- folder for saving test plots./output/predictions/val/plots- folder for saving validation plots./output/predictions/train/data- folder for sving train data./output/predictions/test/data- folder for saving test data./output/predictions/val/data- folder for saving val data
.. code-block:: python
def prepare_output_dir(OUTPUT_PATH):
"""
-- the function prepare_output_dir takes the `OUTPUT_PATH` as an argument and removes all files in the output directory using os.walk method.
Args:
:param str OUTPUT_PATH: path to output folder
How to use: prepare_output_dir(OUTPUT_PATH)
"""
.. code-block:: python
def load_trained_model(MODEL_PATH, device):
"""
--Uploading trained model
agrs:
:param str MODEL_PATH = path to model directorium
:param device = torch device CPU or CUDA
How to use: model=load_trained_model(MODEL_PATH, device)
"""
.. code-block:: python
def get_criteria():
"""
-- Gives the criterion and mse_metric
How to use: criterion, mseMetric=get_criteria()
"""
.. code-block:: python
def remove_padded_values_and_filter(folder_path):
"""
-- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves
Args:
:param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'
How to use:
if __name__ == "__main__":
folder_path = "./dataset/test" # Change this to your dataset folder
remove_padded_values_and_filter(folder_path)
"""
.. code-block:: python
def plot_function(target_x, target_y, context_x, context_y, pred_y, var, target_test_x, lcName, save = False, flagval=0, isTrainData = None, notTrainData = None):
"""
-- Defines the plots of the light curve data and predicted mean and variance, and it should be imported separately
Args:
:param context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
:param context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
:param target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
:param target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
:param target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
:param pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
:param var: An array of shape BATCH_SIZE x 400 that contains the variance of predictions at target_test_x points.
"""
.. code-block:: python
def load_test_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to Test data
How to use: testLoader=load_test_data(DATA_PATH_TEST)
"""
.. code-block:: python
def load_train_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to train data
How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)
"""
.. code-block:: python
def load_val_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to VAL data
How to use: valLoader=load_val_data(DATA_PATH_VAL)
"""
.. code-block:: python
def plot_light_curves_from_test_set(model, testLoader, criterion, mseMetric, plot_function, device):
"""
-- Ploting the transformed light curves from test set
Args:
:param model: Deterministic model
:param testLoader: Uploaded test data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
How to use: testMetrics = plot_light_curves_from_test_set(model, testLoader, criterion, mseMetric, plot_function, device)
"""
.. code-block:: python
def save_test_metrics(OUTPUT_PATH, testMetrics):
"""
-- saving the test metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param testMetrics: returned data from ploting function
How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
"""
.. code-block:: python
def plot_light_curves_from_train_set(model, trainLoader, criterion, mseMetric, plot_function, device):
"""
-- Ploting the transformed light curves from train set
Args:
:param model: Deterministic model
:param trainLoader: Uploaded trained data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
How to use: trainMetrics = plot_light_curves_from_train_set(model, trainLoader, criterion, mseMetric, plot_function, device)
"""
.. code-block:: python
def save_train_metrics(OUTPUT_PATH, testMetrics)
"""
-- saving the train metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param trainMetrics: returned data from ploting function
How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
"""
.. code-block:: python
def plot_light_curves_from_val_set(model, valLoader, criterion, mseMetric, plot_function, device):
"""
-- Ploting the transformed light curves from validation set
Args:
:param model: Deterministic model
:param valLoader: Uploaded val data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
How to use: valMetrics = plot_light_curves_from_val_set(model, valLoader, criterion, mseMetric, plot_function, device)
"""
.. code-block:: python
def save_val_metrics(OUTPUT_PATH, valMetrics):
"""
-- saving the validation metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param valMetrics: returned data from ploting function
How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
"""
- PREDICTION AND PLOTTING THE TRANSFORMED DATA, IN ONE PDF FILE
PREDICTION_onePDF.py
We use this module for prediction and plotting of models of transformed data. All curves will be plotted in one PDF file. This module contains the following functions that must be executed in order.
Before running this script, you must create the following folders in the directory where your Python notebook is located:
./output/predictions/train/plots-- folder for saving training plots./output/predictions/test/plots-- folder for saving test plots./output/predictions/val/plots-- folder for saving validation plots./output/predictions/train/data-- folder for sving train data./output/predictions/test/data-- folder for saving test data./output/predictions/val/data-- folder for saving val data
.. code-block:: python
def clear_output_dir(output_path):
"""
-- Removes all files in the specified output directory.
Args:
:param str output_path: The path to the output directory.
How to use: clear_output_dir(OUTPUT_PATH)
"""
.. code-block:: python
def load_model(model_path, device):
"""
--Loads a trained model from disk and moves it to the specified device.
Args:
:param str model_path: The path to the saved model.
:param str or torch.device device: The device to load the model onto, CPU or CUDA
How to use: model = load_model(MODEL_PATH, device)
"""
.. code-block:: python
def get_criteria():
"""
-- Gives the criterion and mse_metric
How to use: criterion, mseMetric=get_criteria()
"""
.. code-block:: python
def remove_padded_values_and_filter(folder_path):
"""
-- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves
Args:
:param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'
How to use:
if __name__ == "__main__":
folder_path = "./dataset/test" # Change this to your dataset folder
remove_padded_values_and_filter(folder_path)
"""
.. code-block:: python
dev plot_function(target_x, target_y, context_x, context_y, pred_y, var, target_test_x, lcName, save = False, flagval=0, isTrainData = None, notTrainData = None):
"""
-- Defines the plots of the light curve data and predicted mean and variance
Args:
context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
var: An array of shape BATCH_SIZE x 400 that contains the variance of predictions at target_test_x points.
"""
.. code-block:: python
def load_test_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to Test data
How to use: testLoader=load_test_data(DATA_PATH_TEST)
"""
.. code-block:: python
def load_train_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to train data
How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)
"""
.. code-block:: python
def load_val_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to VAL data
How to use: valLoader=load_val_data(DATA_PATH_VAL)
"""
.. code-block:: python
def plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function, device):
"""
-- ploting the test set in range [-2,2]
Args:
:param model: model
:param testLoader: Test set
:param criterion: criterion
:param mseMetric: mse Metric
:param plot_function: defined above
:param device: Torch device, CPU or CUDA
how to use: testMetrics=plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function, out_pdf_test, device)
"""
.. code-block:: python
def save_test_metrics(OUTPUT_PATH, testMetrics):
"""
-- saving the test metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param testMetrics: returned data from ploting function
How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
"""
.. code-block:: python
def plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function, device):
"""
-- Ploting the transformed light curves from train set
Args:
:param model: Deterministic model
:param trainLoader: Uploaded trained data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
How to use: trainMetrics=plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function, device)
"""
.. code-block:: python
save_train_metrics(OUTPUT_PATH, testMetrics)
"""
-- saving the train metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param trainMetrics: returned data from ploting function
How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
"""
.. code-block:: python
def plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function, device):
"""
-- Ploting the transformed light curves from val set
Args:
:param model: Deterministic model
:param valLoader: Uploaded val data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
How to use: valMetrics = plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function, device)
"""
.. code-block:: python
def save_val_metrics(OUTPUT_PATH, valMetrics):
"""
-- saving the val metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param valMetrics: returned data from ploting function
How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
"""
- PREDICTION AND PLOTTING THE DATA IN ORIGINAL DATA RANGE, EACH CURVE INDIVIDUALLY
PREDICTION_Original_mjd.py
We use this module to predict and plot the model in the original range of data. All curves are plotted individually. This module contains the following functions that must be executed in order.
Before running this script, you must create the following folders in the directory where your Python notebook is located:
./output/predictions/train/plots-- folder for saving training plots./output/predictions/test/plots-- folder for saving test plots./output/predictions/val/plots-- folder for saving validation plots./output/predictions/train/data-- folder for sving train data./output/predictions/test/data-- folder for saving test data./output/predictions/val/data-- folder for saving val data
.. code-block:: python
def prepare_output_dir(OUTPUT_PATH):
"""
-- the function prepare_output_dir takes the OUTPUT_PATH as an argument and removes all files in the output directory using os.walk method.
Args:
:param str OUTPUT_PATH: path to output folder
How to use: prepare_output_dir(OUTPUT_PATH)
"""
.. code-block:: python
def load_trained_model(MODEL_PATH, device):
"""
--Uploading trained model
agrs:
:param str MODEL_PATH = path to model directorium
:param str device = torch device CPU or CUDA
How to use: model=load_trained_model(MODEL_PATH, device)
"""
.. code-block:: python
def get_criteria():
"""
-- Gives the criterion and mse_metric
How to use: criterion, mseMetric=get_criteria()
"""
.. code-block:: python
def remove_padded_values_and_filter(folder_path):
"""
-- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves
Args:
:param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'
How to use:
if __name__ == "__main__":
folder_path = "./dataset/test" # Change this to your dataset folder
remove_padded_values_and_filter(folder_path)
"""
.. code-block:: python
def load_trcoeff():
"""
-- loading the original coefficients from pickle file
How to use: tr=load_trcoeff()
"""
.. code-block:: python
def plot_function2(tr,target_x, target_y, context_x, context_y, yerr1, pred_y, var, target_test_x, lcName, save = False, isTrainData = None, flagval = 0, notTrainData = None):
"""
-- function for ploting the light curves
context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
yerr1: Array of shape BATCH_SIZE x NUM_measurement_error that contains the measurement errors.
pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
var: An array of shape BATCH_SIZE x 400 that contains the variance of predictions at target_test_x points.
tr: array of data in pickle format needed to backtransform data from [-2,2] x [-2,2] to MJD x Mag
"""
.. code-block:: python
def load_test_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to Test data
How to use: testLoader=load_test_data(DATA_PATH_TEST)
"""
.. code-block:: python
def load_train_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to train data
How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)
"""
.. code-block:: python
def load_val_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to VAL data
How to use: valLoader=load_val_data(DATA_PATH_VAL)
"""
.. code-block:: python
def plot_test_data(model, testLoader, criterion, mseMetric, plot_function, device, tr):
"""
-- Ploting the light curves from test set in original mjd range
Args:
:param model: Deterministic model
:param testLoader: Uploaded test data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
:param tr: trcoeff from pickle file
How to use: testMetrics=plot_test_data(model, testLoader, criterion, mseMetric, plot_function2, device, tr)
"""
.. code-block:: python
def save_test_metrics(OUTPUT_PATH, testMetrics):
"""
-- saving the test metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param testMetrics: returned data from ploting function
How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
"""
.. code-block:: python
def plot_train_light_curves(trainLoader, model, criterion, mseMetric, plot_function, device, tr):
"""
-- Ploting the light curves from train set in original mjd range
Args:
:param model: Deterministic model
:param trainLoader: Uploaded trained data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
:param tr: trcoeff from pickle file
How to use: trainMetrics=plot_train_light_curves(trainLoader, model, criterion, mseMetric, plot_function2, device,tr)
"""
.. code-block:: python
def save_train_metrics(OUTPUT_PATH, testMetrics):
"""
-- saving the train metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param trainMetrics: returned data from ploting function
How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
"""
.. code-block:: python
def plot_val_curves(model, valLoader, criterion, mseMetric, plot_function, device, tr):
"""
-- Ploting the light curves from val set in original mjd range
Args:
:param model: Deterministic model
:param valLoader: Uploaded val data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
:param tr: trcoeff from pikle file
How to use: valMetrics=plot_val_curves(model, valLoader, criterion, mseMetric, plot_function2, device,tr)
"""
.. code-block:: python
def save_val_metrics(OUTPUT_PATH, valMetrics):
"""
-- saving the val metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
valMetrics: returned data from ploting function
How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
"""
- PREDICTION AND PLOTTING THE DATA IN ORIGINAL DATA RANGE, IN ONE PDF FILE
PREDICTION_onePDF_original_mjd.py
We use this module to predict and plot the model in the original range of data. All curves are plotted in one PDF file. This module contains the following functions that must be executed in order.
Before running this script, you must create the following folders in the directory where your Python notebook is located:
./output/predictions/train/plots-- folder for saving training plots./output/predictions/test/plots-- folder for saving test plots./output/predictions/val/plots-- folder for saving validation plots./output/predictions/train/data-- folder for sving train data./output/predictions/test/data-- folder for saving test data./output/predictions/val/data-- folder for saving val data
.. code-block:: python
def clear_output_dir(output_path):
"""
-- Removes all files in the specified output directory.
Args:
:param str output_path: The path to the output directory.
How to use: clear_output_dir(OUTPUT_PATH)
"""
.. code-block:: python
def load_trained_model(MODEL_PATH, device):
"""
--Uploading trained model
agrs:
:param str MODEL_PATH = path to model directorium
:param str device = torch device CPU or CUDA
How to use: model=load_trained_model(MODEL_PATH, device)
"""
.. code-block:: python
def get_criteria():
"""
-- Gives the criterion and mse_metric
How to use: criterion, mseMetric=get_criteria()
"""
.. code-block:: python
def remove_padded_values_and_filter(folder_path):
"""
-- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves
Args:
:param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'
How to use:
if __name__ == "__main__":
folder_path = "./dataset/test" # Change this to your dataset folder
remove_padded_values_and_filter(folder_path)
"""
.. code-block:: python
def load_trcoeff():
"""
-- loading the original coefficients from pickle file
How to use: tr=load_trcoeff()
"""
.. code-block:: python
def plot_function2(tr,target_x, target_y, context_x, context_y, yerr1, pred_y, var, target_test_x, lcName, save = False, isTrainData = None, flagval = 0, notTrainData = None):
"""
-- function for ploting the light curves. It needs to be uploaded separately
context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
yerr1: Array of shape BATCH_SIZE x NUM_measurement_error that contains the measurement errors.
pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
var: An array of shape BATCH_SIZE x 400 that contains the variance of predictions at target_test_x points.
tr: array of data in pickle format needed to backtransform data from [-2,2] x [-2,2] to MJD x Mag
"""
.. code-block:: python
def load_test_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to Test data
How to use: testLoader=load_test_data(DATA_PATH_TEST)
"""
.. code-block:: python
def load_train_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to train data
How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)
"""
.. code-block:: python
def load_val_data(data_path):
"""
-- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches
Args:
:param str data_path: path to VAL data
How to use: valLoader=load_val_data(DATA_PATH_VAL)
"""
.. code-block:: python
def plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function2, device,tr):
"""
-- ploting the test set in original range
Args:
:param model: model
:param testLoader: Test set
:param criterion: criterion
:param mseMetric: mse Metric
:param plot_function2: defined above
:param device: Torch device, CPU or CUDA
:param tr: trcoeff from pickle file
how to use: testMetrics=plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function2, device,tr)
"""
.. code-block:: python
def save_test_metrics(OUTPUT_PATH, testMetrics):
"""
-- saving the test metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
testMetrics: returned data from ploting function
How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
"""
.. code-block:: python
def plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function2, device,tr):
"""
-- Ploting the light curves from train set in original mjd range
Args:
:param model: Deterministic model
:param trainLoader: Uploaded trained data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
:param tr: trcoeff from pickle file
How to use: trainMetrics=plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function2, device,tr)
"""
.. code-block:: python
def save_train_metrics(OUTPUT_PATH, testMetrics):
"""
-- saving the train metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param trainMetrics: returned data from ploting function
How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
"""
.. code-block:: python
def plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function2, device,tr):
"""
-- Ploting the light curves from val set in original mjd range
Args:
:param model: Deterministic model
:param valLoader: Uploaded val data
:param criterion: criterion
:param mseMetric: Mse Metric
:param plot_function: plot function defined above
:param device: torch device CPU or CUDA
:param tr: trcoeff from pickle file
How to use: valMetrics=plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function2, device,tr)
"""
.. code-block:: python
def save_val_metrics(OUTPUT_PATH, valMetrics):
"""
-- saving the val metrics as json file
Args:
:param str OUTPUT_PATH: path to output folder
:param valMetrics: returned data from ploting function
How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
"""
Frequently Asked Questions
Q What should my input data look like?
A: The input data should have three columns: mjd - Modified Julian Date or time, mag - magnitude, and magerr - magnitude error
Q Do I need to run all four prediction modules?
A: No, it is enough to run only one prediction module, depending on what you want on the final plots. There are four options for prediction and plotting namely:
- all curves are plotted separately and plots contain transformed data.
- all curves are plotted in one pdf document and contain transformed data
- all curves are plotted separately and the plots contain the original data.
- all curves are plotted in one pdf document and contain original data
Q Can the package be used for other uses outside of astronomy?
A: Yes, the package can be used for different types of time series analysis.
Licence
MIT License
Copyright (c) 2024 Andjelka Kovacevic, Marina Pavlovic, Aman Raju, Nikola Mitrovic - Andric, Iva Cvorovic - Hajdinjak, Dragana Ilic, SER-SAG in-kind team from Serbia
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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