Corr function for Dataframe and Series

[risenW]

A Tensorflow based function to calculate Corr for series and columns, similar to Pandas Corr function.

[JhennerTigreros]

Is there any update in this feature?

[risenW]

No one is actively working on this currently. Would like to work on it?

[JhennerTigreros]

Yes I would like it. When you talk about TF-based, do you mean using TF as a support to perform the calculations with the Tensor methods they provide?

[risenW]

Yes, that's exactly what I meant. Alright, I'll assign you to this. Thanks!

[JhennerTigreros]

I suppose that can be a generic method that Dataframe and Series inherit from Generic module and verify in this module what type of structure is an apply correct way to calculate the correlation, right?

[risenW]

The generic module is used for more low level methods. So it should be separated, and also, the way you compute Dataframe Corr is slightly different than Series, I believe one is pairwise (Dataframe) and the other is with another series.

[JhennerTigreros]

Yes, you are rigth. I'm do some research yesterday, at the moment I implement the pearson method to calculate the corr in Series. I want to refactor the math functions and cumulative operations that currently use in the base code to use TF-built in methods to gain performance in large datasets. Currently they are using math.js library.

[JhennerTigreros]

For example the std function:

  std() {
        if (this.dtypes[0] == "string") {
            throw Error("dtype error: String data type does not support std operation")
        }

        let values = []
        this.values.forEach(val => {
            if (!(isNaN(val) && typeof val != 'string')) {
                values.push(val)
            }
        })
        let std_val = std(values) //using math.js
        return std_val

    }

Can be change to

std() {
  if (this.dtypes[0] == "string") {
    Error("dtype error: String data type does not support std operation")
  }

  let values = []

  values.forEach(val => {
    (!(isNaN(val) && typeof val != 'string')) {
      .push(val)
    }});

  let tensor = tf.tensor1d(values, this.dtypes[0]);

  return parseFloat(tf.moments(tensor).variance.sqrt().arraySync());
}

[JhennerTigreros]

For example the std function:

  std() {
        if (this.dtypes[0] == "string") {
            throw Error("dtype error: String data type does not support std operation")
        }

        let values = []
        this.values.forEach(val => {
            if (!(isNaN(val) && typeof val != 'string')) {
                values.push(val)
            }
        })
        let std_val = std(values) //using math.js
        return std_val

    }

Can be change to

std() {
  if (this.dtypes[0] == "string") {
    Error("dtype error: String data type does not support std operation")
  }

  let values = []

  values.forEach(val => {
    (!(isNaN(val) && typeof val != 'string')) {
      .push(val)
    }});

  let tensor = tf.tensor1d(values, this.dtypes[0]);

  return parseFloat(tf.moments(tensor).variance.sqrt().arraySync());
}

After doing some research, I found out that TF when creating a tensor from an array of values may incur some precision errors in float data that diverge the final result from Std, Variance, and others. The error is around ~ 6.35% of the actual value.

[risenW]

For example the std function:

  std() {
        if (this.dtypes[0] == "string") {
            throw Error("dtype error: String data type does not support std operation")
        }

        let values = []
        this.values.forEach(val => {
            if (!(isNaN(val) && typeof val != 'string')) {
                values.push(val)
            }
        })
        let std_val = std(values) //using math.js
        return std_val

    }

Can be change to

std() {
  if (this.dtypes[0] == "string") {
    Error("dtype error: String data type does not support std operation")
  }

  let values = []

  values.forEach(val => {
    (!(isNaN(val) && typeof val != 'string')) {
      .push(val)
    }});

  let tensor = tf.tensor1d(values, this.dtypes[0]);

  return parseFloat(tf.moments(tensor).variance.sqrt().arraySync());
}

For example the std function:

  std() {
        if (this.dtypes[0] == "string") {
            throw Error("dtype error: String data type does not support std operation")
        }

        let values = []
        this.values.forEach(val => {
            if (!(isNaN(val) && typeof val != 'string')) {
                values.push(val)
            }
        })
        let std_val = std(values) //using math.js
        return std_val

    }

Can be change to

std() {
  if (this.dtypes[0] == "string") {
    Error("dtype error: String data type does not support std operation")
  }

  let values = []

  values.forEach(val => {
    (!(isNaN(val) && typeof val != 'string')) {
      .push(val)
    }});

  let tensor = tf.tensor1d(values, this.dtypes[0]);

  return parseFloat(tf.moments(tensor).variance.sqrt().arraySync());
}

After doing some research, I found out that TF when creating a tensor from an array of values may incur some precision errors in float data that diverge the final result from Std, Variance, and others. The error is around ~ 6.35% of the actual value.

Yes, I notice that as well. Did you try rounding the values down? Seems TFJS increases the precision of floats and that leads to the high error rates.

[JhennerTigreros]

For example the std function:

  std() {
        if (this.dtypes[0] == "string") {
            throw Error("dtype error: String data type does not support std operation")
        }

        let values = []
        this.values.forEach(val => {
            if (!(isNaN(val) && typeof val != 'string')) {
                values.push(val)
            }
        })
        let std_val = std(values) //using math.js
        return std_val

    }

Can be change to

std() {
  if (this.dtypes[0] == "string") {
    Error("dtype error: String data type does not support std operation")
  }

  let values = []

  values.forEach(val => {
    (!(isNaN(val) && typeof val != 'string')) {
      .push(val)
    }});

  let tensor = tf.tensor1d(values, this.dtypes[0]);

  return parseFloat(tf.moments(tensor).variance.sqrt().arraySync());
}

For example the std function:

  std() {
        if (this.dtypes[0] == "string") {
            throw Error("dtype error: String data type does not support std operation")
        }

        let values = []
        this.values.forEach(val => {
            if (!(isNaN(val) && typeof val != 'string')) {
                values.push(val)
            }
        })
        let std_val = std(values) //using math.js
        return std_val

    }

Can be change to

std() {
  if (this.dtypes[0] == "string") {
    Error("dtype error: String data type does not support std operation")
  }

  let values = []

  values.forEach(val => {
    (!(isNaN(val) && typeof val != 'string')) {
      .push(val)
    }});

  let tensor = tf.tensor1d(values, this.dtypes[0]);

  return parseFloat(tf.moments(tensor).variance.sqrt().arraySync());
}

After doing some research, I found out that TF when creating a tensor from an array of values may incur some precision errors in float data that diverge the final result from Std, Variance, and others. The error is around ~ 6.35% of the actual value.

Yes, I notice that as well. Did you try rounding the values down? Seems TFJS increases the precision of floats and that leads to the high error rates.

No, I didn't. I went back to the current implementation, but tomorrow I will try one more time. Yes, TFJS increases the precision, but it also depends on the processor / gpu / browser running the library, so I think it is a compatibility issue

[risenW]

Yea, I think so too. I found this as well:

https://stackoverflow.com/questions/56649680/tensorflow-vs-tensorflow-js-different-results-for-floating-point-arithmetic-comp

Let me know what you come up with.

[JhennerTigreros]

In way to implement the corr methods I notice that DataFrame and Series class has owned isna() method.

Series ->

  isna() {
        let new_arr = []
        this.values.map(val => {
            // eslint-disable-next-line use-isnan
            if (val == NaN) {
                new_arr.push(true)
            } else if (isNaN(val) && typeof val != "string") {
                new_arr.push(true)
            } else {
                new_arr.push(false)
            }
        })
        let sf = new Series(new_arr, { index: this.index, columns: this.column_names, dtypes: ["boolean"] })
        return sf
    }

DataFrame ->

  isna() {
        let new_row_data = []
        let row_data = this.values;
        let columns = this.column_names;

        row_data.map(arr => {
            let temp_arr = []
            arr.map(val => {
                // eslint-disable-next-line use-isnan
                if (val == NaN) {
                    temp_arr.push(true)
                } else if (isNaN(val) && typeof val != "string") {
                    temp_arr.push(true)
                } else {
                    temp_arr.push(false)
                }
            })
            new_row_data.push(temp_arr)
        })

        return new DataFrame(new_row_data, { columns: columns, index: this.index })
    }

I think it's a good idea to move this to the generic NDFrame class to extend some features like align data, something that pandas have in generalizing the operation of both modules.

def isna(self) -> "DataFrame":
        result = self._constructor(self._data.isna(func=isna))
        return result.__finalize__(self, method="isna")

What do you think about that?

[risenW]

Looks interesting. What do you mean by extending features like align data?

Also to be sure, are you proposing we abstract the isna function to generic or an internal function that can be called by the isna function from both Series and Dataframe?

If we have to abstract it, then we have to use a different name, something like __isna(), and this can return values as an array which will be constructed in the Dataframe or Series depending on the caller.

For example, in generic we can have:

 /**
     * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values,
     *  gets mapped to True values. Everything else gets mapped to False values. 
     * @return {Array}
     */
    __isna(is_series=true) {
        let new_arr = []
        if (is_series){
            this.values.map(val => {
                // eslint-disable-next-line use-isnan
                if (val == NaN) {
                    new_arr.push(true)
                } else if (isNaN(val) && typeof val != "string") {
                    new_arr.push(true)
                } else {
                    new_arr.push(false)
                }
            })
        }else{
            let row_data = this.values;
            row_data.map(arr => {
                let temp_arr = []
                arr.map(val => {
                    // eslint-disable-next-line use-isnan
                    if (val == NaN) {
                        temp_arr.push(true)
                    } else if (isNaN(val) && typeof val != "string") {
                        temp_arr.push(true)
                    } else {
                        temp_arr.push(false)
                    }
                })
                new_arr.push(temp_arr)
            })
        }
        return new_arr
    }

and from DataFrame or Series we can call __isna() in the isna() function. Is this what you intend?

UPDATE: Check this abstraction I did here

[JhennerTigreros]

Correctly, I think is good idea to abstract the method to generic module like you say and propose.

I meant if you have two series or dataframes of different sizes and you want to compute the corr function, Pandas first apply df.align(df2) that align the data with smaller object this means clear excess data on the other object and before apply the respective corr function, at the momment I have this:

       if (kwargs["min_periods"] === undefined || kwargs["min_periods"] === 0) {
            kwargs["min_periods"] = 1;
        }

        if (this.size < kwargs["min_periods"]) {
            return NaN;
        }

        if (kwargs["min_periods"] < 0 && kwargs["min_periods"] > this.size) {
            throw new Error(`Value Error: min_periods need to be in range of [0, ${this.size}]`);
        }

        if (other !== undefined) {
          let [ left, right ] = this.__align_data(other, { "join": "outer", "axis": 0, "inplace": false})
          let valid_index = utils.__bit_wise_nanarray(left.isna().values, right.isna().values)

          if (valid_index.length !== 0) {
            left = left.iloc(valid_index)
            right = right.iloc(valid_index)
          }

          if (left.__check_series_op_compactibility(right)) {
            let f = this.__get_corr_function(kwargs["method"]);
            return f(left, right);
          }
        }

[steveoni]

Correctly, I think is good idea to abstract the method to generic module like you say and propose.

I meant if you have two series or dataframes of different sizes and you want to compute the corr function, Pandas first apply df.align(df2) that align the data with smaller object this means clear excess data on the other object and before apply the respective corr function, at the momment I have this:

       if (kwargs["min_periods"] === undefined || kwargs["min_periods"] === 0) {
            kwargs["min_periods"] = 1;
        }

        if (this.size < kwargs["min_periods"]) {
            return NaN;
        }

        if (kwargs["min_periods"] < 0 && kwargs["min_periods"] > this.size) {
            throw new Error(`Value Error: min_periods need to be in range of [0, ${this.size}]`);
        }

        if (other !== undefined) {
          let [ left, right ] = this.__align_data(other, { "join": "outer", "axis": 0, "inplace": false})
          let valid_index = utils.__bit_wise_nanarray(left.isna().values, right.isna().values)

          if (valid_index.length !== 0) {
            left = left.iloc(valid_index)
            right = right.iloc(valid_index)
          }

          if (left.__check_series_op_compactibility(right)) {
            let f = this.__get_corr_function(kwargs["method"]);
            return f(left, right);
          }
        }

Is the corr. calculating the correlation within dataframe columns or between two dataframe (or it is calculating both)

[JhennerTigreros]

Correctly, I think is good idea to abstract the method to generic module like you say and propose. I meant if you have two series or dataframes of different sizes and you want to compute the corr function, Pandas first apply df.align(df2) that align the data with smaller object this means clear excess data on the other object and before apply the respective corr function, at the momment I have this:

       if (kwargs["min_periods"] === undefined || kwargs["min_periods"] === 0) {
            kwargs["min_periods"] = 1;
        }

        if (this.size < kwargs["min_periods"]) {
            return NaN;
        }

        if (kwargs["min_periods"] < 0 && kwargs["min_periods"] > this.size) {
            throw new Error(`Value Error: min_periods need to be in range of [0, ${this.size}]`);
        }

        if (other !== undefined) {
          let [ left, right ] = this.__align_data(other, { "join": "outer", "axis": 0, "inplace": false})
          let valid_index = utils.__bit_wise_nanarray(left.isna().values, right.isna().values)

          if (valid_index.length !== 0) {
            left = left.iloc(valid_index)
            right = right.iloc(valid_index)
          }

          if (left.__check_series_op_compactibility(right)) {
            let f = this.__get_corr_function(kwargs["method"]);
            return f(left, right);
          }
        }

Is the corr. calculating the correlation within dataframe columns or between two dataframe (or it is calculating both)

At the moment I'm calculating correlation within dataframe columns, I've pearson and kendall tau-b working now. See #26

[steveoni]

Ok. that's cool. Great job :+1:

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[JhennerTigreros]

Its been a while since I can work on this. @steveoni or @risenW Any update for this implementation?. Now I can return to tackle this issue 💪🏽

[risenW]

Its been a while since I can work on this. @steveoni or @risenW Any update for this implementation?. Now I can return to tackle this issue 💪🏽

Update on this? We have released the TS version, so you can update this issue